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PEH:Miscible Processes

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Petroleum Engineering Handbook

Larry W. Lake, Editor-in-Chief

Volume V – Reservoir Engineering and Petrophysics

Edward D. Holstein, Editor

Chapter 14 – Miscible Processes

Edward D. Holstein, Holstein Consulting and Fred I. Stalkup, PetroTel Inc.

Pgs. 1261-1308

ISBN 978-1-55563-120-8
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Miscible injection is a proven, economically viable process that significantly increases oil recovery from many different types of reservoirs. Fieldwide projects have been implemented in fields around the world, with most of these projects being onshore North American fields. Many of these projects are quite mature, making the recovery and production-rate benefits well established. As a result, the ability to predict recovery levels, rate improvements, costs, and resulting economics can now be considered proven and reliable. The purpose of this chapter is to introduce some fundamental concepts about miscible displacement, suggest some methods of predicting the benefits of miscible injection, and present a few field examples that demonstrate what has been accomplished with miscible injection.

Introductory Concepts

To put miscible flooding into perspective, it is instructive to compare the performance of a miscible flood to that of a waterflood. Although it is impossible to define a "typical" flood, the simplistic example shown in Fig. 14.1 introduces the physics of the process and illustrates the level of incremental recovery and sweep often achieved with miscible flooding. This example is based on simulation results for the Means Lower San Andres reservoir in west Texas.[1]

The upper diagram in Fig. 14.1 shows a pattern element in the reservoir (with an injector on the left and a producer on the right) near the end of a waterflood. The initial average oil saturation, Soi, of 70% at the start of the waterflood was reduced to an average of 44%, Sorwf, at the end. The 44% accounts for much higher saturations near the edges of the pattern and in lower-permeability strata not contacted by water. The waterflood recovers 37% of the original oil in place (OOIP) and sweeps approximately 80% of the reservoir.

The average Sorwf in those regions swept by water is 38%. The oil that remains in the water-swept part of the reservoir is trapped as a discontinuous phase within the pore space. The primary goal of a miscible flood is to recover part of this trapped residual oil. However, solvent can sometimes also displace oil from upper regions of the reservoir not swept or poorly swept by water because of gravity-driven water "slumping."

The lower diagram in Fig. 14.1 shows the same reservoir element near the completion of a miscible flood started near the end of the waterflood. In practice, a solvent flood is often initiated before completing the waterflood—or, in some cases, even before starting the waterflood. The schematic shows that solvent sweeps only part of the reservoir previously swept by water, and only a portion of the residual oil in the solvent-swept regions is recovered. The average oil saturation after the miscible flood (Sorm) of 36% includes (1) higher oil saturations remaining in regions near the edge of the pattern element and in lower-permeability strata bypassed by both water and solvent and (2) residual oil in that part of the reservoir swept by water but not by solvent. The miscible flood recovered an incremental 11% OOIP over and above waterflooding results. Expressed another way, the incremental recovery was 18% of the oil remaining after waterflooding.

The solvent sweeps approximately 50% of the pattern, compared to approximately 80% for water. In regions swept by solvent, oil saturation is reduced from an average value of 38% to 24%. The average oil saturation of 24% in solvent-swept regions accounts for oil saturations lower than 24% near the injector, as well as higher saturations near the producer. At the pore level, solvent displaces some, but not all, of the waterflood residual oil.

Three solvent-injection strategies commonly used in commercial miscible-flooding applications are slug injection, water-alternating-gas (WAG) injection, and gravity-stable injection. The slug process usually involves continuous injection of approximately 0.2 to 0.4 hydrocarbon pore volumes (HCPV) of solvent that, in turn, is displaced by water or dry solvent.[2] The WAG process involves alternately injecting small volumes (0.01–0.04 HCPV) of water and solvent.[2] The total amount of solvent injected usually ranges from 0.2 to 0.6 HCPV.[3] As with the slug process, the final drive fluid is usually water or dry solvent. It is commonly accepted that alternate injection of small slugs of water decreases solvent mobility and leads to increased solvent sweep efficiency.[4] Experience with many projects has indicated that field-management processes improve with time, and additional volumes of injectant can be justified to further increase recovery.

For some pinnacle reefs and steeply dipping reservoirs with high vertical communication, it is advantageous to inject less-dense solvent at the top of the reservoir in a gravity-stable displacement process. Solvent sweep efficiency and oil recovery are quite high, provided that there is sufficient vertical continuity.[5][6][7][8][9]

The two major factors that affect the performance of a miscible flood are oil-displacement efficiency at the pore level and sweep efficiency on the field scale. Oil displacement can be explained using the schematic on the left side of Fig. 14.2, which shows solvent flowing from left to right through a pore space. The displacement process involves several mechanisms.[2][10][11] One is direct miscible displacement of oil by solvent along higher-permeability pore paths. Additionally, part of the oil initially bypassed (on the pore level) by solvent can later be recovered through oil swelling that occurs as solvent dissolves in the oil, or by extraction of oil into solvent. Swelling and extraction take place as solvent continues to flow past the initially bypassed oil. These can be significant mechanisms in field processes and together may account for as much as 20 to 30% of the total incremental recovery. Oil-displacement efficiency is affected by solvent composition and pressure. Solvents can be designed that give very high displacement efficiencies at the pore level.[1][12][13][14][15][16][17]

The right side of Fig. 14.2 shows that, on a field scale, sweep efficiency is affected by viscous fingering and solvent channeling through high-permeability streaks. Gravity override can sometimes occur because solvent is usually less dense than the oil it is displacing.[18][19] When vertical communication is high, solvent tends to gravity segregate to the top of a reservoir unit and sweep only the upper part of that zone. Although gravity override can be a problem in reservoirs having good vertical communication (such as Judy Creek[20] and Prudhoe Bay[21]), it is not usually a serious problem for west Texas carbonates,[22] which tend to be more stratified and have poor vertical communication.

As discussed in specific field examples, sweep efficiency on the field scale is usually the single most important factor affecting performance of a miscible flood. Sweep efficiency can be increased to some extent by reducing well spacing, increasing injection rate, reconfiguring well patterns, increasing solvent-bank sizes, and modifying the ratio of injected water to injected solvent (WAG ratio).

Fig. 14.3 presents part of a considerable body of laboratory evidence that solvent effectively displaces oil from contacted regions of the reservoir. The graph of oil recovery as a function of total pore volumes of fluid injected shows the results of a laboratory coreflood conducted at conditions corresponding to the Sharon Ridge reservoir in west Texas. The waterflood recovered approximately 40% OOIP. A CO2 flood that followed increased oil recovery to approximately 80% OOIP, demonstrating that CO2 can displace a large portion of the residual oil remaining after a waterflood. Sorm was 10%; the WAG ratio for the miscible flood was 1.

The schematics at the bottom of Fig. 14.3 illustrate the pore-level recovery mechanisms discussed earlier (Fig. 14.2). At the end of the waterflood, residual oil is a discontinuous phase that occupies approximately 40% of the pore space. Early in the miscible flood [3.0 to 3.5 total pore volumes (PV) injected], some of this oil has been miscibly displaced by solvent from the higher-permeability flow path (on the pore scale). However, some oil also has been initially bypassed by solvent. Note that this bypassing at the pore level is much different from solvent bypassing, which can occur at the field scale because of larger-scale reservoir heterogeneities. As depicted in the schematic corresponding to late in the flood (to 7.0 total PV injected), part of this locally bypassed oil is subsequently recovered by extraction and swelling that takes place as solvent continues to flow past the bypassed oil. In this case, approximately 30% of the total amount of oil recovered by the CO2 flood was recovered by extraction and swelling.

Designing a Miscible Flood

Determining Miscibility

True miscible displacement implies that injected and displaced phases mix in all proportions without forming interfaces or two phases. The single-phase condition also implies that the solvent eventually displaces all resident oil from the pore space that it invades. Although some fluids, such as propane, fulfill this definition, most solvents available for oilfield use form two distinct phases over a broad range of mixtures and pressures when combined with reservoir oils. However, when the same solvent displaces oil at reservoir temperature and above a suitably high pressure in a long, small-diameter (slim) sandpacked tube, a miscible-like displacement occurs. Slimtube experiments are designed to make the displacement essentially 1D with 100% volumetric sweep by the solvent front.

Fig. 14.4 shows a series of hypothetical slimtube experiments. In these experiments, the solvent displaces oil from the fully oil-saturated slimtube at several pressures. Oil recovery is shown after 1.2 PV of injection for each pressure. Oil recovery increases with pressure up to approximately 95 to 98% and then increases very little thereafter. The pressure at which the break in the recovery curve occurs is said to be the minimum miscibility pressure (MMP). If the displacements had been conducted at constant pressure and with increasing enrichment by components such as ethane, propane, and butane, the break over would have been at the minimum miscibility enrichment (MME). Above the MMP or MME, the displacement is said to be "multiple-contact" or "dynamically" miscible. The increasing recovery with pressure or solvent enrichment results from in-situ mass transfer of components between solvent and resident oil. Each pressure increase produces an equilibrium mixture that becomes compositionally similar at the MMP or MME. Methane, methane enriched with C2 –C4 hydrocarbons, CO2, N2, and flue solvent will all give compositionally enhanced displacements under the right conditions of pressure, temperature, and oil composition. The MMP or MME can be significantly different for each of these solvents.

A slimtube is not representative of the performance in reservoir rock because it does not account for the effects of factors such as gravity segregation and reservoir heterogeneity on volumetric sweep. Fig. 14.5 shows how ethane enrichment of methane affected oil recovery in an experiment conducted by Chang et al.[23] with live reservoir fluid in a 34.6-in.-long, relatively homogeneous Bentheimer sandstone core. The experimental conditions resulted in a single, gravity-overriding tongue of solvent in the core.[23] Oil recovery continued to increase from enrichment levels well below to well above the MME determined from slimtube displacements without evidencing a pronounced break over. In other words, compositional enhancement continued to increase recovery well above the slimtube MME for this gravity-dominated displacement.

Jerauld[24] reported a similar finding by use of a compositional reservoir simulation of a one-fourth nine-spot pattern element (Fig. 14.6). Recovery continued to increase from well below to well above the simulator-predicted MME of 0.65.

Choosing a Candidate

A decision to implement a miscible flood in a particular field will usually consist of a sequential approach.

First is the screening stage. Data in the literature allow a reasonable estimate of MMP or MME, Sorm, amount of solvent required, and operating costs. This information is adequate to determine if a reservoir is a candidate.

MMP and MME Guidelines and Correlations. Table 14.1 gives some rough guidelines for achieving MMP or MME with different injection solvents. For hydrocarbon solvent enriched with the C2 –C4 hydrocarbons, MMP in the range 1,500 to 3,000 psi might be expected for mid-API-gravity oils, depending on oil and solvent compositions and reservoir temperature.

The vaporizing-solvent process (see later sections) is applicable to high-gravity oils. MMP typically is greater than 3,500 psi and is usually greater than 4,500 psi (and can be much greater).

CO2 flooding is applicable with medium-gravity oils. At temperatures less than approximately 125°F, MMP can be as low as 1,200 psi. MMP increases with temperature.

Fig. 14.7[25] shows one graph for a similar correlation that was developed for condensing-solvent-drive MMP. It also appears to be useful for condensing/vaporizing drives. Other graphs are given in Benham, Dowden, and Kunzman.[14]

In field projects in which the displacement was above either the MMP or the MME, residual oil saturation determined by coring behind the solvent front varied from approximately 3 to 10% PV.[26]

Second, a more thorough assessment may require acquiring laboratory data on Sorwf , Sorm, and MMP or MME. Analog data from nearby fields may be adequate for these values and may indeed be adequate for evaluating a project without additional work. Occasionally, some type of field pilot may be thought necessary to address such questions as displacement and sweep efficiencies (most small pilots do not produce results to reliably predict these factors), injectivity of miscible fluids, and existence of fractures or very-high-permeability layers that would prevent the miscible fluids from contacting a significant volume of the reservoir.

Third, some type of simulation will normally be done to incorporate reservoir and fluid characteristics unique to the field in question. The type of simulator used will depend on the amount and quality of the characterization data available and the perceived risks of the project that justify the costs of the various types of simulation that can be done.

Several factors should be considered in assessing the economic viability of a miscible project:

  • What miscible fluids are available, and what is the corresponding MMP or MME?
  • Miscible fluids commonly considered are hydrocarbon solvents such as enriched methane, CO2, N2, and, less often, exhaust or flue gases. Assessments of hydrocarbon solvents should include near-term lost revenue because of delays in sales (if any) and the ultimate amount of solvent that will remain in the reservoir at abandonment. MMP and MME will also narrow the number of solvents that may be applicable to a specific field.
  • Is the MMP sufficiently below overburden pressure, or are adequate enrichment fluids available for the field in question?
  • In many instances, the MMP for a given solvent may exceed the overburden pressure of a formation. Where MMP is less than overburden, the question becomes whether a high-enough injection rate can be achieved to satisfy a reasonable project life.
  • Are near-miscible recoveries high enough to support a project?
  • As discussed earlier, significant additional recovery may be possible without reaching the slimtube MMP.
  • What is the incremental recovery vs. the solvent slug size (Fig. 14.9)?
  • Numerical simulations can provide sufficient insight to evaluate the economics of a project. For most projects, slug size can be refined further during the actual flood (usually increased) when actual performance can be used to modify initial projections.
  • Which WAG ratio will be most effective?
  • Simulations can give a good initial estimate and will be good enough for defining the costs of a WAG project. Several different WAG schemes have been used in practice. These include:
  • An initial slug of miscible solvent followed by a low WAG ratio tapering to a high ratio.
  • A constant WAG ratio that tapers to a high ratio near the end of the project.
  • Monthly adjustments in individual-pattern WAG ratios based on the observed performance of offset producers.
  • In all cases, surveillance practices after the start of a project include periodic (monthly to quarterly) studies of the gas/oil-ratio (GOR) and water/oil-ratio (WOR) trends in producing wells and the indicated adjustments needed in WAG ratios in offsetting injectors to achieve desired performance.
  • Will water- and solvent-injection rates change?
  • Several projects have experienced reduced water injectivity (20% or more) after the WAG process was started.[27]
  • Some projects have experienced solvent injectivity higher than water, while others have seen significant decreases.[27]
  • Such decreases in injectivity may significantly affect project economics.
  • Will separation of the solvent from produced fluids be necessary?
  • This is a problem usually associated with CO2 and N2 projects. Breakthrough of solvents (which occurs early and grows with time) will contaminate produced hydrocarbon gases. Separation is required to remove contaminants before sale. The investment and operating cost of separation facilities and compression for reinjecting the recovered miscible materials should be included in the economic assessment of the project.
  • In some instances, the amount of hydrocarbon gases produced may not justify its recovery, and reinjection of the total solvent stream may be a more practical solution.
  • How much solvent must be purchased, and how much should be recycled?
  • Simulation results should provide sufficient insight on the rates of solvent breakthrough to answer this question. Predicted purchased volumes are generally in the 40 to 50% range of the total injection required, with the remainder being recycled solvent.
  • Initial estimates of solvent needs are usually in the 40 to 60% range of HCPV. This figure tends to increase over the project life as reservoir management practices improve sweep and reduce costs, thus sustaining the economic viability of the project longer than originally forecast.
  • What is the time from first expenditure to first incremental production (Fig. 14.10)?
  • Capital outlays for equipment, drilling, and modifications to existing facilities will be made before the project start.
  • In addition, experience shows that there is a delay from the time solvent injection starts to the first significant production response. This delay roughly corresponds to injection of 0.05 to 0.1 HCPV of solvent. In most cases, most of the purchased solvent is injected before significant recovery of incremental oil. Such a delay, of course, is inevitable because waterflood residual oil that is displaced by the solvent front has to travel from injector to producer. Often, there is no delay because incremental increases in oil production result from immediate improvements made to operations. There also can be a substantial delay before the peak incremental oil rate is attained, amounting very roughly to 0.1 to 0.2 HCPV of solvent injected. Project economics should include the impact of this timing.
  • Another important feature of field tests that is consistent with mechanistic concepts and simulations is that solvent breakthrough usually occurs concurrently with the first production of the incremental-process oil or shortly thereafter. This signifies only a small, clean oil bank ahead of the advancing solvent front because of solvent fingering caused by adverse mobility ratios, gravity override, or permeability stratification. Much of the banked-up oil is located around the sides of these fingers and will be recovered with additional solvent injection (see Figs. 14.1 and 14.2).
  • Are additional wells needed?
  • Many projects have included the drilling of infill wells to provide more effective injectors, better volumetric sweep, and the productivity needed for good project economics. This applies to relatively low-cost environments in mature operating areas and has not been necessary or practical when wellbores are in good condition; in other, higher-cost operating areas; or where the reservoir characteristics did not require additional development.

Compositional Numerical Simulation

Prediction of a miscible flood is best done with a compositional reservoir simulator. The simulation must be able to predict the phase behavior as well as the sweep behavior in the reservoir to forecast such quantities as incremental oil recovery, miscible-solvent requirement, and solvent utilization efficiency and to optimize such variables as solvent composition, operating pressure, slug size, WAG ratio, injection-well placement, and injection rate. These topics are discussed in more detail in subsequent sections.

Phase Behavior

Methods for characterizing the reservoir and injected fluids vary from several approximation methods to rigorous component analysis.

Representation of Phase Behavior With Ternary and Pseudoternary Diagrams. Ternary diagrams and pseudoternary diagrams have been used for decades to visualize conceptually the phase behavior of injection-fluid/crude-oil systems. This is done by representing multicomponent fluids or mixtures by three pseudocomponents and then plotting fluid compositions in the interior of an equilateral triangle with apexes that represent 100% of each pseudocomponent and where the side opposite an apex represents 0% of that pseudocomponent. Usually, the three pseudocomponents represent a fraction of low-molecular-weight materials, a fraction of intermediate-molecular-weight materials, and a fraction of the higher-molecular-weight materials. For example, the low-molecular-weight fraction might include methane and nitrogen and perhaps CO2 if CO2 is the primary injection solvent. The intermediate-molecular-weight pseudocomponent might include the C2 –C6 hydrocarbons and perhaps CO2 if the CO2 is a constituent of an otherwise hydrocarbon injection solvent. The higher-molecular-weight pseudocomponent in this scheme would be the leftover C7+ fraction. There is no particularly "right" way to divide a fluid into three pseudocomponents. Different injection processes may be better represented by one type of grouping vs. another, and different groupings may give somewhat different insights into phase-behavior mechanisms.

Pseudoternary diagrams apply rigorously only to true ternary systems, and a strictly ternary analogy may give a somewhat misleading view of the mass-transfer mechanisms that result in compositional enhancement. Even so, a pseudoternary diagram is still a useful way to represent some complex phase-behavior concepts that are not so easily visualized otherwise.

A ternary diagram represents phase behavior at a constant temperature and pressure. Fig. 14.11 is a traditional pseudoternary-diagram representation of phase behavior for the pseudo-components C1, C2–6, and C7+. It has the following characteristics.

There is a bubblepoint curve representing oil mixtures at their bubblepoint and a dewpoint curve representing solvent mixtures at their dewpoint. These curves come together at a critical composition. The dashed lines, called tie lines, connect liquid and solvent compositions that are in equilibrium. All mixtures inside the region bounded by the bubblepoint and dewpoint curves consist of two phases. The tie line that passes through a given mixture composition gives the equilibrium solvent and liquid compositions for this mixture where it intersects the dewpoint and bubblepoint curves. The tie line that just passes through the critical composition is called the critical tie line. All mixtures outside the region bounded by the dewpoint and bubblepoint curves are single phase.

The Vaporizing-Solvent-Drive Process. Fig. 14.11 shows how compositions change in situ when a lean injection solvent displaces an oil represented by point A, whose composition lies just to the right of the limiting tie line.

The injection solvent identified on the C1–C2–6 side of the triangle has a high methane content. When this solvent mixes with the reservoir fluid, an overall composition such as M1 may result. M1 is in the two-phase region of the diagram and consists of dewpoint solvent G1 and bubblepoint liquid L1. As more solvent is injected, solvent G1, formed in the first contact, is pushed ahead, where it contacts fresh reservoir fluid. Upon mixing, solvent G1 and the reservoir fluid form another overall mixture, M2, which consists of dewpoint solvent, G2, and bubblepoint liquid, L2. Solvent G2 then flows ahead and contacts fresh reservoir fluid, forming an overall mixture M3, which consists of dewpoint solvent G3 and bubblepoint liquid L3. In this way, solvent at the displacing front is progressively enriched to the critical composition P, which is first-contact miscible with the reservoir fluid.

In the vaporizing-solvent-drive process, compositional enhancement occurs by the injection solvent vaporizing intermediate-molecular-weight hydrocarbons from the oil and enriching the composition at the solvent front. For pseudoternary phase behavior, as long as the reservoir-fluid composition lies to the right of the limiting tie line through the critical point, multiple-contact miscibility can be achieved. If oil composition should lie to the left of the critical tie line, solvent enrichment can occur only to the composition of dewpoint solvent lying on the tie line that can be extended to pass through the oil composition. For example, if reservoir oil B were being displaced by the injection solvent, enrichment of the solvent front could occur only up to dewpoint solvent G2. Although multiple-contact miscibility is not achieved, efficient immiscible vaporization may still occur (depending on the compositions actually achieved) and should be evaluated for effectiveness.

The mechanism for compositional enhancement described above can be effective for other solvents besides lean hydrocarbon solvent. N2 and flue-gas solvent can give compositionally enhanced displacements by this mechanism, although with different MMPs. CO2 also achieves compositionally enhanced displacement by a similar mechanism, although for lower temperatures the mechanism may be one of liquid/liquid extraction rather than vaporization.

The conceptual argument given above indicates that compositions in the solvent/oil transition zone lie along the dewpoint curve until the critical composition is reached so that no transition-zone compositions lie inside the two-phase region. Because of mixing caused by diffusion and reservoir flow mechanisms, this is not the case in practice. Transition-zone compositions cut into the two-phase region, causing some solvent-flood residual oil to be left behind the solvent front.

The Condensing-Solvent-Drive Process. Fig. 14.12 shows another mechanism whereby compositional enhancement can occur in a solvent flood. The oil is a bubblepoint liquid lying on the bubblepoint curve. For this phase behavior, an injection solvent that has composition A on the C1–C2–6 side of the triangle is completely miscible with the oil because the line connecting oil with composition A passes through only the single-phase region. However, according to the traditional ternary-diagram view of phase behavior, solvents that have compositions between A and B can develop multicontact miscibility in the following manner.

Solvent B lies just to the right of the critical tie line. When this solvent mixes with the original oil, an overall composition such as M1 may result, which is in the two-phase region. M1 consists of dewpoint solvent G1 and bubblepoint liquid L1, which are in equilibrium. Injection of fresh injection solvent B pushes the equilibrium solvent G1 ahead and contacts residual oil around the injector that now has composition L1. B mixes with L1 and forms a new overall composition in the two-phase region, M2, which splits into a new equilibrium solvent G2 and equilibrium liquid L2. Continued injection of solvent B pushes the new solvent G2 out of the way, and B mixes with liquid L2 to form a new overall mixture M3. Thus, solvent B successively contacts oil around the injector and enriches the oil along the bubblepoint curve by condensation of the intermediate-molecular-weight hydrocarbons that were used to enrich C1–C2–6 mixtures to composition B. This enrichment of the oil proceeds until the enriched oil reaches the critical composition P at the critical point. Composition P is then first-contact miscible with solvent B. According to this concept, there is a transition zone of contiguously miscible liquid compositions from the original oil composition to the critical composition.

This method of solvent flooding historically was called the condensing-solvent-drive process because condensation of the intermediate-molecular-weight hydrocarbons into the oil was thought to be the mechanism responsible for the development of multicontact miscibility. In this process, miscibility is generated and propagated through the porous medium at the rear of the transition zone.

According to the pseudoternary diagram of Fig. 14.12, if the composition of injection solvent were to the left of the critical tie line, the displacement would be immiscible because the oil could never be enriched to the critical composition. For example, if solvent C were injected, the oil could be enriched only to composition L1 on the tie line that passes through C when the tie line is extended. Further contact of L1 with C only gives new overall mixtures that are on the tie line, so equilibrium solvent G1 on the tie line ends up displacing oil L1, and G1 and L1 are immiscible. The criterion for condensing-solvent-drive multiple-contact miscibility is that the injection-solvent composition must lie to the right of the critical tie line on the ternary diagram.

For most oils, however, the mechanism described previously for an enriched-solvent displacement is too simplified. Compositional enhancement occurs by a mixed mechanism that has both vaporizing and condensing features, as described below.

The Condensing/Vaporizing Process. Fig. 14.13 shows a pseudoternary diagram for a condensing/vaporizing displacement in which the equilibrium vapor and liquid compositions were calculated for a simulated slimtube displacement with a compositional simulator. The two-phase region has an hourglass shape. There is what looks like a condensing lobe in which vapor and liquid are trying to come together at a critical composition similar to condensing-solvent drive. However, before this happens, vapor and liquid compositions begin to diverge at the trailing part of the displacement.

Zick[16] was first to explain this behavior. He deduced that in addition to condensation of intermediate-molecular-weight hydrocarbons from the injection solvent, vaporization of mid-range hydrocarbons from the oil also played an important role. Zick explained this mechanism in the following way:

The easiest way to understand the condensing/vaporizing mechanism is to consider an oil/solvent system composed of essentially four groups of components. The first group consists of the lean components, such as methane, nitrogen, and carbon dioxide, which usually have equilibrium K-values greater than one. The second group consists of the light intermediate components, such as ethane, propane, and butane, which are the enriching components present in the injection solvent. The third group contains the middle intermediates, which are present in the oil but not significantly present in the injection solvent. These are components that can be vaporized from the oil. The lightest component in this group typically ranges from butane to decane, depending on the injection solvent composition. The heaviest component in this group cannot be defined precisely, but it might be around C30. The fourth group consists of everything else, i.e., those heavy components in the oil which are very difficult to vaporize.[16]

When the enriched solvent comes into contact with the oil, the light intermediates condense from the solvent into the oil, making the oil lighter. The equilibrium solvent is more mobile than the oil, so it moves ahead and is replaced by fresh injection solvent, from which more light intermediates condense, making the oil even lighter. If this kept occurring until the oil was light enough to be miscible with the injection solvent, it would constitute the condensing-solvent-drive mechanism. However, this is unlikely to occur within a real reservoir oil. As the light intermediates are condensing from the injection solvent into the oil, the middle intermediates are being stripped from the oil into the solvent. Because the injection solvent contains none of these middle intermediates, they cannot be replenished in the oil. After a few contacts between the oil and the injection solvent, the oil becomes essentially saturated in the light intermediates, but it continues to lower the middle intermediates, which are stripped out and carried ahead by the mobile solvent phase. The light intermediates of the injection solvent cannot substitute for the middle intermediates the oil is losing. So after the first few contacts make the oil lighter by net condensation of intermediates, subsequent contacts make the oil heavier by net vaporization of intermediates. Once this begins to occur, the oil no longer has a chance of becoming miscible with the solvent. Ultimately, all the middle intermediates are removed, and the residual oil will be very heavy, containing only the heaviest, nonvolatile fraction and the components present in the injection solvent.

If the mechanism stopped there, a considerable amount of oil would remain unrecovered. However, there are further steps to the mechanism. Consider the oil in place slightly downstream from the injection point. The first solvent it will see will not be the injection solvent, but the equilibrium solvent. This relatively lean solvent essentially will be injection solvent that has lost most of its light intermediates and picked up a very small amount of middle intermediates. There will be very little mass transfer between this solvent and the fresh oil. The solvent that follows, however, will be richer. Eventually, the solvent that comes through will be solvent that has passed over oil that was saturated in the light intermediates. Therefore, this solvent will have approximately the same amount of light intermediates as the injection solvent. However, it will also contain a small amount of middle intermediates that it stripped from the oil over which it passed. Thus, it actually will be a little richer than the original injection solvent. The oil that sees this solvent will receive slightly more condensable intermediates than did the oil just upstream. Before the vaporization process takes over and again makes it heavier, this oil will become slightly lighter than the upstream oil had become.

This process continues farther downstream. The farther downstream, the richer the solvent that eventually comes through because it will have passed over an increasing amount of residual oil, allowing it to pick up increasing amounts of middle intermediates. This is beginning to sound like the vaporizing-solvent-drive mechanism, in which a lean injection solvent passes over an oil rich in intermediates, vaporizing the intermediates and becoming richer and richer until it becomes rich enough to be miscible with the original oil. There is a significant difference, however. The solvent in the condensing/vaporizing mechanism does not become rich enough to be miscible with the original oil. The original oil does not have to be rich in intermediates, nor does it even have to be undersaturated, both of which are necessary conditions for developing a vaporizing-solvent-drive mechanism. Instead, the solvent develops only enough richness by the vaporization part of the mechanism so that it nearly generates a condensing-solvent-drive mechanism with the original oil. The intermediates that were originally present in the solvent, plus those that were stripped from the oil, condense when the solvent encounters fresh oil downstream. This condensation proceeds in a manner very much like the condensing-solvent-drive mechanism. A sharp transition zone develops and propagates, and multicontact miscibility is almost achieved before the condensation process reverts to the vaporization process. The vaporization results in a trail of residual oil being left behind the moving transition zone, although the saturation level of the residual oil supplies subsequent solvent with the middle intermediates necessary to continue the propagation of the transition zone. The intermediates are vaporized from the residual oil, carried upstream into and beyond the transition zone, condensed there, and again become part of the residual oil after the transition zone has passed.

The condensing region is at the leading edge of the enriched-solvent displacement. The vaporizing region, with a small saturation of residual oil, is at the trailing end. In between is the sharp, two-phase transition zone, the two phases of which are almost—but not quite—miscible. The propagation of the sharp transition zone results in a very efficient, "apparently miscible" displacement, even though miscibility is not actually developed (except possibly, and only speculatively, as the displacement front travels to infinite distances, relative to dispersion length scales, downstream of the injection point). The sharpness of the transition zone deteriorates rapidly as either the pressure or the enrichment of the injection solvent falls below some critical value, resulting in the reduced displacement efficiencies typical of immiscible displacements.

Mixing by diffusion and flow mechanisms affects how close the mixed mechanism gets to multicontact miscibility. The greater the mixing, the farther vapor and liquid compositions remain apart in the neck of the hourglass, which results in larger solvent-flood residual oil.

In all the processes just described, equilibrium solvent and oil properties become more similar as compositions become more similar and approach the critical composition. This causes the interfacial tension between the solvent and oil to decrease as the solvent and oil compositions become more alike. This in turn causes the capillary number for oil displacing solvent to increase.

The capillary number is defined as


where k = permeability, Δpg = pressure gradient through the displacing phase, and σ = interfacial tension.

Below a threshold value of capillary number, solvent-flood residual oil and solvent/oil relative permeability remain unchanged. Above the threshold, residual oil begins to decrease with increasing capillary number, and the solvent and oil relative permeability curves begin to straighten, ultimately becoming straight lines at very high values of the capillary number. These changes may have contradictory effects on a displacement. For a given equilibrium oil and solvent composition near the critical composition, solvent-flood residual oil saturation may be a bit lower than it would because of phase-behavior effects alone. However, solvent mobility may be higher because of the more favorable solvent relative permeability, which may result in somewhat poor sweep. The overall effect needs to be evaluated with a simulator that accounts for changing capillary number.

Prediction of Phase Behavior With Equations of State. In practice, vapor/liquid reservoir phase behavior is calculated by an equation of state (EOS). Refer to the chapter on Thermodynamics and Phase Behavior in the General Engineering section of this Handbook for more detail on EOSs. The two most common EOSs that have been used for oil-recovery solvent-injection processes are the Peng-Robinson EOS[28] and the Soave-Redlick-Kwong EOS.[29] Of the two, the Peng-Robinson EOS seems to be the one most often cited in the literature and is the one discussed in some detail in the remainder of this section. The Soave-Redlick-Kwong EOS is used in a similar manner to predict solvent/oil phase behavior.

Peng and Robinson originally proposed the two-parameter EOS shown next for a pure component:




and RTENOTITLE....................(14.5)

where ω = the component acentric factor, Tc = component critical temperature, and pc = component critical pressure.

For heavier components, where ω > 0.49, the following equation is recommended:


The constants in Eqs. 14.3 and 14.5 are often designated Ωa and Ωb.

Eq. 14.2 represents continuous fluid behavior from the solvent to liquid state, and it can be rewritten as


where RTENOTITLE....................(14.8)

and RTENOTITLE....................(14.9)

Jhaveri and Youngren[30] adapted a procedure used by Peneloux et al.[31] and modified the original Eq. 14.2 to include a third parameter to allow more-accurate volumetric predictions, which is recommended for solvent/oil simulations. The third parameter does not change the vapor/liquid equilibrium conditions determined by the unmodified, two-parameter equation. Instead, it modifies the phase volumes by making a translation along the volume axis. Eqs. 14.10 and 14.11 give the modified three-parameter equation:



where s is the volumetric shift parameter.

For mixtures:




and RTENOTITLE....................(14.15)

In Eq. 14.13, ij is the binary interaction coefficient that characterizes the binary formed by components i and j. Eqs. 14.11 through 14.14 apply both to pure components and to lumped pseudocomponents that represent two or more pure components in complex mixtures.

The following expression derived from thermodynamic relationships and the EOS allows calculation of the fugacity, fj, of component j in a mixture:


Thus, by satisfying the equilibrium condition RTENOTITLE, vapor/liquid equilibrium ratios can be calculated, and flash calculations can be made to calculate the compositions of vapor and liquid in equilibrium, molar splits, and volumes.

Solution of the EOS does not calculate phase viscosities directly. This is done from some external calculation once the phase compositions and densities are known. A commonly used calculation for liquid-mixture viscosity is the Lohrenz-Bray-Clark method, which requires the critical volumes of each component or pseudocomponent in the mixture.[32] Refer to chapters in the General Engineering section for more information on estimating viscosities for gas and oil.

To use Eqs. 14.2 through 14.16 for calculating the phase behavior and properties of solvent compositional processes in oil recovery, the following steps must be taken to "characterize" the fluid system in question:

  • Analyze the oil composition. This can be done by distillation or chromatographic methods. An extended analysis through at least C25+ is preferred. The advantage of distillation is that molecular weight, boiling point, and density can be measured on the distillation cuts.
  • Represent the multicomponent reservoir fluid by an appropriate division into pure components and pseudocomponents. Pure components through C 5 plus three to five pseudocomponents usually will suffice. It may be possible to reduce the number of pure components and pseudocomponents further by combining similar components.
  • Make an initial assignment of critical pressure and temperature, acentric factor, critical volume (or critical compressibility), volumetric shift parameter, and interaction parameters for each component and pseudocomponent.
  • Tune the above properties for the pseudocomponents by comparing predicted phase behavior and properties with suitable experimental data.
  • Methods for dividing into pseudocomponents and estimating critical properties, shift parameters, and binary interaction coefficients are described in detail in Whitson and Brule.[33]

Because of the approximations inherent in an EOS as well as the approximations required to represent a multicomponent reservoir fluid in a tractable form, it should be expected that phase-behavior properties and equilibrium compositions predicted with an EOS will depart from measured values over the range of composition and pressure conditions anticipated in a reservoir simulation. For this reason, additional adjustment of EOS parameters will be required for predictions to represent experimental measurements adequately. These adjustments usually are made by regression.

Reservoir oils usually are subjected to routine pressure/volume/temperature (PVT) experiments that give the volumetric and phase-behavior information necessary for predicting conventional recovery methods such as solution solvent drive or waterflooding. Experiments such as constant-composition expansion, differential liberation, constant-volume depletion, and separator tests provide black-oil properties. Other PVT experiments are more specific for solvent injection. These include swelling tests and multiple-contact experiments.

The swelling experiment is sometimes called a pressure-composition diagram determination. Injection solvent is added to reservoir oil in increments to give mixtures that contain increasing amounts of injection solvent. After each addition of solvent, the saturation pressure is measured at reservoir temperature. Overall composition of these mixtures ranges from that of black oil to compositions up to and beyond near-critical conditions (i.e., overall compositions that traverse a range from bubblepoint to dewpoint mixtures at reservoir temperature). Thus, the swelling experiment provides some PVT and phase-equilibrium information on mixture ranges that might reflect compositions as solvent displaces oil through the reservoir. It provides information on the saturation pressure of injection-solvent/oil mixtures, the swelling or increase in oil formation volume factor as solvent is added, the composition of the critical mixture, and the liquid saturation vs. pressure in the two-phase region of the diagram.

Multiple-contact tests seek to simulate the solvent/oil multiple contacting that occurs in a reservoir. A forward multicontact experiment tries to simulate multicontacting in a vaporizing-solvent drive. A reverse multicontact experiment tries to simulate the multicontacting that occurs in a purely condensing-solvent drive. The experiments give information concerning equilibrium-phase volumes and compositions.

In a reverse-contact experiment, the PVT cell is charged with the reservoir fluid at the desired pressure and temperature, and an increment of injection solvent is added sufficient to form a two-phase mixture (or a three-phase mixture in some tests). The phases are allowed to equilibrate, and phase volumes are measured. The solvent phase is then displaced from the cell, and oil and solvent compositions are measured. The procedure is repeated, with injection of a new increment of injection solvent introduced into the cell to contact equilibrium oil left after the first contact.

In the forward-contact experiment, the oil phase is displaced after the first contact, and the remaining equilibrium-solvent phase in the cell is contacted with a fresh increment of reservoir oil.

The objective of tuning is to ensure that the EOS predicts fluid properties and phase equilibrium compositions accurately over the range of pressure, temperature (if this varies), and composition that one expects to encounter in a simulation. If the simulation is for a solvent compositional process, then at a minimum the EOS should predict properties and phase equilibrium for the range of injection-solvent/oil mixtures and pressures encountered in the simulation study. It also should predict adequately for any black-oil conditions expected in the simulation (e.g., waterflooding or pressure depletion before solvent injection) and for the separator conditions expected.

Pedersen et al.[34] observed that an EOS tuned to match a specific set of data may not give reliable predictions for other data not included in the tuning process. However, when both sets of data are included in the tuning process, the prediction for either one may not be quite as good as for tuning against these data individually.

It seems prudent that at a minimum, there should be differential depletion data, separator tests, and swell data to tune an EOS against for making solvent-compositional simulations. Swelling tests are necessary when near-critical compositions are expected in the simulation, and it is necessary for the swell tests to explore this composition region. Swell tests with several different injected-solvent compositions might be warranted if optimization of the solvent composition is an objective of the simulation study.

The value added by multiple-contact tests is unclear. These are the most difficult and expensive of the experiments discussed earlier, yet they provide direct measurements of vapor/liquid equilibrium compositions and molar splits for a composition path that at least crudely mimics the development of compositions at the leading or trailing edges of the solvent/oil transition zone, which is, of course, what the simulator is trying to calculate. However, for the condensing/vaporizing process, multiple-contact experiments do not give compositions that are very near the critical point.

Although they are difficult and expensive to run, slimtube tests give a direct verification of the ability of the EOS to predict MMP or MME. If the EOS after regression of parameters does not predict slimtube MMP or MME, further adjustment of parameters is required.

For additional information on EOS, refer to the chapter on Phase Behavior in the General Engineering section of this Handbook.

Prediction of Compositionally Enhanced Solvent Flood Behavior

The compositional reservoir simulator is the preferred simulator for predicting compositionally enhanced solvent flood reservoir behavior. This simulator calculates the flow in up to three dimensions of solvent, oil, and water phases as well as n components in the solvent and oil phases. It also computes the phase equilibrium of the oil and solvent phases (i.e., the equilibrium compositions and relative volumes of the solvent and oil phases) in each gridblock of the simulator. In addition, it computes solvent- and oil-phase densities. The equilibrium compositions and densities are calculated with an EOS. From knowledge of the phase compositions and densities, solvent and oil viscosity and other properties such as interfacial tension are estimated from correlations. See the chapter on Reservoir Simulation in the Reservoir Engineering and Petrophysics section for more information on compositional reservoir simulation.

A compositional simulator is the most mechanistically accurate simulator for solvent compositional processes. When the EOS is tuned properly to appropriate experimental data, it computes realistic phase behavior. Thus, the appropriate phase behavior for flooding with enriched hydrocarbon solvent, lean hydrocarbon solvent, N2, and CO2 all can be taken into account. Compositional simulators predict the effect of changing pressure and injection-solvent composition on a displacement without the need to enter approximations into the simulator for these effects (except as the EOS itself is an approximation). The compositional simulator is capable of computing realistic behavior when pressure is well below the MMP of the injection solvent, is near but still below the MMP, or is well above the MMP. For this reason, it is ideally suited to study optimum operating conditions.

In addition to these advantages, a compositional simulation, to a large degree, removes the need for a user-defined miscible-flood residual oil saturation, as it naturally computes the amount of residual oil left after the interaction of phase behavior and dispersion and distributes this residual saturation realistically as a varying saturation instead of an input, constant saturation.

A compositional simulation can have other aspects of mechanistic reality besides phase behavior. The mechanisms of molecular diffusion and convective dispersion may be included in the equations solved by the simulator. Although grid-refinement sensitivity (described later), or numerical dispersion, may dwarf the effects of these mechanisms in many simulations, they may be important to include in the finely gridded reference simulations (also described later).

Another physical mechanism that can be included in compositional simulations is the effect of interfacial tension (IFT) on solvent/oil relative permeability and capillary pressure. Although one cannot readily foresee the impact of a particular mechanism in the complex compositional simulation of solvent flooding, inclusion of the IFT mechanism seems prudent.

When an appropriate relative permeability treatment is included, compositional simulation predicts realistic solvent trapping, especially the trapping of solvent by crossflowing oil. Oil crossflow into a solvent-swept zone immiscibly displaces the solvent in a compositional simulation and leaves the solvent as a residual saturation consistent with the phase behavior.

The primary disadvantages of a compositional simulator are the degree of grid refinement often required to compute oil recovery with satisfactory accuracy and the computing time required for fine-grid simulations. These factors generally preclude using a compositional simulator directly for full-field simulations unless some kind of scaling-up technique is used to transfer the information developed from fine-grid reference-model simulations on a limited reservoir scale to coarse-grid simulations on the full-field-model scale. The predicted benefit of compositionally enhanced solvent flooding can be substantially in error if the simulation is made directly with a full-field model with typical coarse grids. This is illustrated by Fig. 14.14, which shows the results of an enriched-solvent-drive reservoir study.[24] In this figure, simulations were made for two one-fourth nine-spot models that represented the same reservoir description. One model had a fine grid (30×30×31 cells in the x-, y-, and z- directions); the other had the same grid as that used in the full-field model (5×5×17). The incremental recovery in this figure is the difference between solvent-flood and waterflood simulations in each model. The direct full-field simulation overpredicted incremental recovery by a factor of two.

There also are some additional data requirements for predicting solvent trapping and solvent relative permeability hysteresis that are not found in black-oil waterflood simulations. Solvent trapping, reference models, and scaleup are discussed next.

Solvent Trapping and Solvent Relative Permeability Hysteresis

In most compositionally enhanced solvent displacements, some of the solvent will be trapped permanently in the reservoir and will not be produced. This happens when water is used to drive a solvent slug and the oil displaced by the solvent. Solvent is trapped by advancing water much like oil is left as a residual in a waterflood. Solvent also can be trapped by oil that crossflows into a previously solvent-swept zone.

Laboratory data indicate that there is little dependence of the trapped solvent saturation on whether water or oil is the trapping phase.[35] These data also show that the magnitude of the trapped solvent saturation is insensitive to whether the measurement is made at reservoir or ambient conditions, on core plugs or composites of core plugs, or on native state or extracted cores.

The magnitude of the trapped-solvent saturation depends on the magnitude of the maximum solvent saturation present when the solvent is trapped. This is illustrated in Fig. 14.15, which shows data for Prudhoe Bay cores.[35] In these experiments, various initial solvent saturations were established before the cores were flooded with either water or oil. There is a substantial degree of scatter in the data, but the trapped-solvent saturation clearly depends on the initial solvent saturation. Moreover, within the data scatter, the trapping is nearly independent of the liquid phase doing the trapping and even independent of a small oil saturation if it should happen to be present as a third phase.

According to Jerauld,[35] the relationship between trapped-solvent saturation and the maximum solvent saturation at the location at which trapping occurs is generally well represented by a "zero slope" adaptation of the Land curve:


In this equation, RTENOTITLE is the solvent trapped when the rock is 100% solvent saturated, and RTENOTITLE is the maximum solvent saturation at the location where trapping occurs. According to Eq. 14.17, if solvent subsequently is mobilized from this location only to be trapped again when the flowing saturation is lower, the retrapped solvent still attains the trapped saturation achieved at the previous maximum saturation. If on subsequent remobilization the solvent saturation should reach a higher value than the previous maximum, the trapped saturation will attain a new and higher value, according to Eq. 14.17.

The maximum trapped-solvent saturation, Sgrmax, when the rock initially is 100% solvent saturated depends strongly on both porosity and clay content, or microporosity. This is illustrated in Fig. 14.16 for data from various sandstones, which show a generally increasing trend for RTENOTITLE with decreasing porosity.[35]

Because of the nature of solvent trapping, an important mechanism to be accounted for in WAG flooding is solvent relative permeability hysteresis. This is illustrated in Fig. 14.17. Consider solvent injection for the first WAG cycle. Solvent is the nonwetting phase, and solvent injection is a drainage process. The solvent relative permeability depends only on the solvent saturation. The solvent may be displacing only oil in the presence of connate water for a secondary flood, only water in the presence of waterflood residual oil for a tertiary flood, or both oil and water for a partially waterflooded reservoir. All these situations are approximated by the same solvent primary-drainage curve, and on the first WAG cycle the solvent relative permeabilities follow the primary-drainage curve AE. If at the end of the first solvent cycle a volume of solvent has been injected so that point B on the primary-drainage curve has been reached at a given location in the reservoir, the solvent saturation at this location will be SgB.

Now consider water injection on the first WAG cycle. Assume that the solvent saturation at the location in question remains SgB. (Actually, the saturation may increase somewhat as water at first displaces solvent past this location.) When water reaches this location, it will drive the solvent down to a trapped saturation, SgtD, at point D according to the trapped-solvent- vs. maximum-solvent-saturation relationship for the rock. This is an imbibition process, and the solvent relative permeability follows the curve BD.

When solvent is injected on the second WAG cycle, the solvent relative permeability follows the curve DB because Land’s evidence demonstrates that imbibition relative permeability often is nearly reversible.[36] It is important to take this hysteresis into account in a WAG simulation because the imbibition relative permeability is substantially less than the primary-drainage relative permeability and will cause the mobility ratio to be lower and the displacement more effective than would be the case with primary-drainage relative permeability only.

If the solvent saturation at the location in question never reaches SgB, solvent relative permeability will stay on the curve BD during the subsequent second-WAG-cycle water slug. If such a large solvent slug is injected that SgB is exceeded at this location, solvent relative permeability will once again follow the primary-drainage curve, perhaps to point E, and attain a new maximum solvent saturation at this location, SgE. Then, on the subsequent water cycle, the solvent relative permeability will follow a new imbibition curve, EC, and solvent will be trapped at a new trapped-solvent saturation, SgtC, according to the trapped-solvent- vs. maximum-solvent-saturation relationship.

Fine-Grid Reference Models

Grid-refinement sensitivity is an extremely troublesome problem in many compositionally enhanced solvent simulations. The problem manifests itself by the predicted behavior changing as the grid is refined (i.e., as the gridblocks become smaller and smaller). This behavior can be caused by truncation error or numerical dispersion that results from representing derivatives by finite differences; by the inability to accurately resolve the size of solvent tongues or fingers with large gridblocks; and by the inability to represent with large gridblocks some features of reservoir description that have an important effect on solvent sweep, such as discontinuous shales, thin high-permeability strata, or thief zones.

Fig. 14.18 shows the incremental recovery computed for two different 3D models, one representing one-eighth of a nine-spot pattern, the other representing one-fourth of a nine-spot. Each model had a different geostatistical distribution of correlated permeability with scattered, discontinuous shales represented by zero vertical permeability between gridblocks. Permeability and porosity were scaled up by the renormalization method from the model with the smallest gridblocks to the other models.[37]

The base model for the one-eighth nine-spot has a grid of 20×20×40. Gridblocks were 93 ft on a side and 1 ft thick. The gridding of the one-fourth nine-spot model was 20×20×80, with gridblocks also 93 ft on a side and 1 ft thick.

Incremental recovery in this figure is plotted vs. 1/NX, where 1/NX is the dimensionless x -direction gridblock size. However, in this problem the dimensionless gridblock sizes in the other two directions also vary directly with the x -direction gridblock size. It is apparent that as the gridblock size is refined, the predicted incremental recovery decreases for what is supposed to be the same reservoir problem.

Fig. 14.18 illustrates the importance of minimizing grid-refinement error and explicitly including reservoir-description details that affect flow in an important way. Generally, minimizing the error from grid refinement and accounting for important reservoir-description details adequately requires small gridblocks. Layers that are 1 ft or no more than a few feet thick and have at least 20 to 40 lateral gridblocks between wells are desirable. Unfortunately, such fine gridding is not feasible for full-field simulations, for most 3D simulations of a single pattern, or perhaps even for some 3D repeating elements of a pattern. Because of this, field predictions need to be made in two steps—with reference models that can be gridded finely enough to accomplish the objectives summarized above, and with scaleup models that incorporate the information derived from reference models into field predictions that account for fieldwide reservoir description, multiple patterns, and operating realities and constraints.

Although it is desirable to make 3D reference-model simulations gridded so finely that the computed answer is adequately close to the converged answer, the discussion above shows that in general, it may not be feasible to do this. A reasonable alternative may be to make finely gridded 2D cross-section simulations to study the grid-refinement issue because for many problems, grid refinement has a larger effect on the computed outcome than the areal effects captured by a coarser-gridded 3D model. Variable-width 2D cross sections sometimes adequately represent the behavior of 3D pattern-segment models with the same fine gridding. In these cross sections, the width is smaller near the injector and producer and increases in the interwell region. This causes flow rate to be greatest near the wells and lowest midway between wells, as it would in a 3D displacement. Even when a fine-grid cross section does not realistically model a fine-grid 3D displacement, it still may predict incremental recovery better than a simulation in a more coarsely gridded 3D model. Moreover, 2D cross-section simulations are well suited for scaleup with the segment and streamline/streamtube models discussed in the next section.

A potential procedure for developing a 3D reference model is first to make a 3D simulation of a pattern element with the finest-grid refinement that is practical. Then, well-to-well cross sections are taken from this model, and the cross sections are refined further. Pseudoproperties are developed for the original cross sections (see Sec. 14.5.4) that predict the performance of the more finely gridded cross sections. Then, these pseudoproperties are used in the moderately gridded 3D model to approximate the effect of further grid refinement.[24]

Scaleup To Predict Full-Field Behavior

The objective of scaleup is to take the behavior predicted from detailed, fine-grid reference models that at best represent only a few wells and a tiny part of the reservoir and transfer it to a model that attempts to represent many wells and the integrated behavior of the entire compositionally enhanced solvent flood (or at least a significant portion of it). Three scaleup methods are (1) development of pseudorelative permeability relations and pseudoproperties for use in coarse-grid, full-field, numerical reservoir simulators; (2) development of segment models that estimate the behavior of pattern-repeating elements and add up the behavior of all the segments of a pattern, as well as the behavior of all the patterns; and (3) development of areal streamtube or streamline models that incorporate 2D vertical cross-section solutions into the streamtubes or streamlines and integrate behavior for all the streamtubes or streamlines.

Pseudorelative Permeability Relations and Pseudoproperties

Jerauld[24] is a good example of the application of this method. Several reference models describe different areas of the field. Water/oil, solvent/oil, and solvent/water pseudorelative permeability relations are developed, along with pseudotrapped-solvent and solvent-flood residual oil values, so that the relevant behavior of the reference models is reproduced by corresponding models that have the same coarse grids as the full-field model. The coarse-grid models, of course, represent the same parts of the full-field model that the reference models represent. The pseudorelative permeability relations may be developed by trial and error, or they may be estimated by various methods from the fluid-flow and saturation values in the reference-model gridblocks.

In practice, the pseudorelative permeability relations and pseudoproperties reproduce behavior of one particular fine-grid simulation (e.g., a particular slug size, WAG ratio, injection-solvent composition, start of injection relative to waterflooding, etc.). Of course, an objective of full-field-model simulations is to study different operating scenarios to optimize the flood; to have much utility, the pseudoproperties must predict behavior for conditions other than those of the particular fine-grid simulation that was used to derive them.

Fig. 14.19 shows how well behavior in one of the coarse-grid models of Jerauld[24] reproduced the behavior of the corresponding fine-grid model for both the waterflood and the compositionally enhanced solvent flood. The fine-grid and corresponding coarse-grid models represented one-fourth of a nine-spot pattern. The grid of the reference model was 30×30×31, which contained layers that ranged from 4 to 8 ft thick. This 3D reference model already contained pseudorelationships that had been derived from cross sections with 1-ft layers by the method described previously. The grid of the coarse-grid model was 5×5×17. Both the timing and level of waterflood and WAG-flood oil recovery are predicted very well.

Although the pseudorelations were developed for a single slug size, WAG ratio, and solvent composition in the reference-model simulation, they reproduced the effect of these variables surprisingly well in subsequent coarse-grid simulations, as seen in Figs. 14.6 and 14.20. The reader is cautioned that this degree of predictability of the pseudorelations may result from the fact that the coarse-grid model still has a relatively large number of gridblocks, especially in the vertical direction, for a full-field model, and thus still retains a large degree of description detail and ability to resolve solvent tongues.

Scaleup With Segment Models

When there are too many patterns to allow a sufficient number of gridblocks between wells for adequate scaleup and predictions by the pseudofunction method, segment-model scaleup may be a suitable choice. This was the case for the enriched-solvent flood at Prudhoe Bay, where there are more than 200 enhanced oil recovery (EOR) and waterflood patterns.[38][39][40]

The segment model is best suited for regular, repeating patterns. Segment-model scaleup divides each pattern into injector/producer segments, as illustrated in Fig. 14.21, for a nine-spot pattern. The dimensionless behavior of each segment is computed using information from suitable reference-model simulations (e.g., dimensionless recovery and solvent production vs. dimensionless injection such as HCPV or displaceable pore volumes). Time rating is accomplished with assumed rates, rates calculated by some other model, or actual injection and production data.

The preferred method for dividing the patterns into segments is with a streamline model that honors actual injection and production rates. Such a model calculates the streamline pattern from injectors to producers, and from this the no-flow boundaries can be determined. Typically, the streamlines for a waterflood are used. Such a model does not assume a balanced, closed boundary system and is able to account for the effects of unbalanced patterns, reservoir heterogeneities, and faulting.

Of course, in an actual flood, the streamlines change with time as they are influenced by injection and production rates and fluid-mobility changes, and dividing a pattern into segments on the basis of a snapshot of streamline distribution is an approximation. It also may be prudent to adjust segments further according to engineering judgment.

A simpler, and more approximate, method for dividing into segments is by geometric construction. Segments are constructed by bisecting the area surrounding the producing wells and connecting these segments to the appropriate injector.

Using the reference-model curves directly can become cumbersome quickly because of such factors as different solvent slug sizes injected into each segment (because of different degrees of throughput into each segment), different amounts of water preinjection, and differing WAG ratios. Reference simulations need to be run for all these variations, and schemes must be developed to interpolate between the various reference curves.

Wingard and Redmond[42] proposed a novel way of transforming the dimensionless performance computed by the reference model by making an analogy with the behavior of a series of stirred tanks. In their procedure, segment performance is calculated over a series of timesteps. The solvent injected during a given timestep mobilizes a given amount of incremental oil and creates a given amount of returned solvent that was not effective in mobilizing oil. The incremental oil mobilized during each timestep and the returned solvent are then produced from each segment according to the dimensionless performance of the reference model. The Wingard and Redmond method calculates the incremental oil recovered by solvent injection vs. time. The total recovery is obtained by superimposing this incremental recovery on a waterflood calculation.

Wingard and Redmond[42] give equations that (1) represent the type of information shown in Fig. 14.9 and derived from reference-model simulations for incremental recovery vs. slug size, (2) represent the increment of returned solvent for each increment of solvent slug injected, and (3) represent the production of each mobilized-oil increment according to the dimensionless injection vs. production performance derived from the reference-model simulations.

The advantage of the segment model is its simplicity and tractability for a large number of patterns. Its major drawback is the inflexibility in accounting for drastic changes in streamline patterns and, thus, changing segment volumes and creation of new segments as wells are converted from producer to injector (or vice versa), as patterns are reconfigured, as wells are recompleted or shut in, or as new wells are drilled. Wingard and Redman discuss these issues and propose approximations to make.

In addition, if solvent-flood performance depends on factors other than just slug size, such as throughput rate, WAG ratio, degree of prior waterflooding, and changing pressure level, approximations must be made to account for these factors.

Scaleup With Streamtube and Streamline Models

In many respects, scaleup with areal streamtube models is similar to scaleup with segment models. First, segments are assigned from injectors to producers—in this case, the streamtubes. The streamtubes are defined with a special model that calculates streamlines.[41][43][44] Typically, the pressure distribution is solved on an underlying grid for a given distribution of total mobility, and the streamlines are calculated for that pressure distribution. Fig. 14.22 illustrates streamlines calculated for 2D areal flow, no-flow boundaries between wells, and the resulting streamtubes for several wells. Refer to the chapter on Reservoir Simulation in the Reservoir Engineering and Petrophysics section for more information on streamtube models.

Emanuel et al.[45] describe a procedure for superimposing fine-grid reference-model solvent-flood simulations on 2D areal streamtubes as follows:

  • Construct a detailed fine-grid geostatistical cross-section reference model that characterizes the permeability and porosity heterogeneity and correlation between wells in the reservoir area of interest. The wells chosen should typify the flow path of the displacing fluid. This selection is judgmental and will depend on reservoir characteristics. The reference model should be highly detailed in the vertical direction to represent measured log or core data as closely as possible. Layers should be 1 or 2 ft thick. The number of gridblocks between wells should be 20 to 100 depending on computational tractability. Although only one gridblock wide, the cross section should be of variable width to represent the shape of the streamtube. This geometry is intended to model the transition from radial flow near the wells to more linear flow midway through the pattern.
  • Simulate the behavior of the process of interest in the reference model. The results of these simulations are reduced to correlations of phase fractional flow at the producer and the total mobility vs. distance from injector to producer as a function of pore volumes or HCPV injected.
  • Map the fractional-flow solution onto each streamtube by (a) determining the total mobility in each tube for the cumulative HCPV injected into each tube, (b) allocating injected volume to each streamtube according to its total mobility (the fluid rate for each injection well can be specified or calculated from the imposed pressure drop and total resistance to flow of all the streamtubes), (c) calculating the incremental HCPV injected into each streamtube for the timestep selected, (d) determining the fractional phases of the produced fluid for the cumulative HCPV injected according to correlations, and (e) summing up the contributions from all the tubes connected to a producing well.

The primary advantage of streamtube models over segment models is that the effect of well-rate changes and changing mobility on the streamlines and resulting areal sweep can be accounted for, provided that the streamlines and streamtubes are updated with time. Even if they are not updated, such a model still gives an estimate of the effect of short streamlines and long streamlines on areal sweep, provided that areal sweep is dominated by viscous forces (i.e., a sufficiently high viscosity/gravity ratio) or by areal heterogeneity. In addition, throughput rates can be estimated from the total mobility of the streamtubes rather than being assumed as in segment models. Otherwise, limitations are similar to those encountered with segment models.

Giordano et al.[46] propose a novel method of mapping the behavior computed by a reference model directly on the streamlines. Their method makes an analogy between oil mobilization and solvent trapping with tracer adsorption and desorption. They convert the incremental solvent EOR performance computed for a 2D cross-section reference model to 1D tracer-model equations that represent adsorption and desorption of the tracer as it flows through the reservoir:


where Ci and Ai are the flowing and adsorbed concentrations of component i, xs is the distance along the streamline from the injection well, and ϕi is the accessible pore volume of component i, which is used as a parameter to scale breakthrough times.

Giordano et al.[46] present equations that, for a series of timesteps, (1) calculate the solvent that is effective in mobilizing incremental oil and leaves it trapped according to an adsorption curve; (2) calculate additional ineffective solvent that is left trapped according to another adsorption curve; (3) calculate production of the remaining solvent that is not adsorbed; and (4) calculate the oil mobilized by a given increment of solvent injected according to a desorption curve. Injected water is calculated as the difference between the total injection rate and the injected-solvent rate; produced water is the difference between the total production rate and the sum of the produced solvent, EOR oil, and waterflood oil.

The power of the Giordano et al.[46] method is its ability to account for the effect of well conversions and shut-ins, infill drilling, and changing well rates on streamline patterns, as well as its ability to avoid the complications of having to recalculate streamtubes. Its limitations, similar to those for streamtube models, are in the complexity of accounting for changes in performance caused by changes in WAG ratio, throughput rate, pressure level, injected-solvent composition, and other operating conditions.

Field Examples

References included in this chapter[19][20][21][38][39][40][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75] present performance information on many different miscible projects. A few examples of these projects are included in this section to illustrate the types of performances that have been observed with the use of CO2, hydrocarbon, and N2 solvents in varying types of reservoirs.

CO2 Projects

There have been more CO2 projects than any other type of miscible flood. The three examples reviewed in this section are considered typical of such applications.

SACROC Four-Pattern Flood.[1][47] This project has been completed. It was thoroughly waterflooded before starting miscible injection. This sequence allows a straightforward evaluation of increased recovery because of miscible displacement.

Fig. 14.23 shows the oil-production rate for the end of the waterflood and the miscible flood. Actual field data are represented by the solid curve, and the forecast decline curve for a continuing waterflood is shown as the dotted curve. The difference between the actual field rate and the forecast waterflood decline represents increased recovery resulting from the miscible project (shaded area); the amount is given in MMSTB. Additional reservoir performance data, including primary plus secondary (P + S), miscible, and total recovery, are given in the upper-right-hand box as a percent OOIP. These data are given in terms of cumulative recovery to date as well as projected ultimate recovery.

After completing the waterflood in 1981, the CO2 flood was initiated with the same wells and injection patterns. The four-pattern area encompasses 600 acres and 19 MMSTB (3.0 million m3) OOIP. The well pattern is an inverted nine-spot with 40-acre well spacing. Shortly after starting CO2 injection, there was an increase in oil-production rate. The EOR of 1.7 MMSTB (0.3 million m3) is equivalent to 9% OOIP, which, when added to primary plus secondary recovery (57% OOIP), gives a total recovery of 66% OOIP. Net CO2 use was 3.2 Mscf/STB of increased recovery (570 std m3/m3).

This project demonstrated that incremental oil can be recovered by a miscible flood after an efficient waterflood. In this case, water injectivity after CO2 injection was higher than during the waterflood, thus enabling oil to be recovered more quickly.

Fig. 14.24 illustrates the comparison of actual miscible flood performance to that predicted with a four-component compositional simulator. The Todd-Longstaff mixing model[67] was used to account for viscous fingering, and phase behavior was represented by a pseudoternary diagram. Two major empirically based physical parameters, Sorm and a viscous-fingering parameter, were used to model local displacement and sweep efficiencies. Sorm was based on laboratory displacement tests using representative samples of reservoir rock and fluids. The first step in simulation was to history match the waterflood. This enabled fine-tuning of the reservoir-description model. The compositional simulator was then used to calculate performance of the miscible flood without further adjustment to any match parameters.

As shown in Fig. 14.24, the simulation of cumulative oil recovery vs. cumulative injection for the miscible flood agrees reasonably well with actual field results. The produced water/oil ratio from the simulation is also in reasonable agreement with field results. Waterflood sweep efficiency was 74%, and the miscible flood sweep efficiency was 44%. These sweep efficiencies were determined from analysis of the simulation studies.

Means San Andres Unit.[1][7][12][21][48] This field, located in the eastern edge of the Central Basin Platform of the Permian Basin, produces primarily from the Permian-aged San Andres formation. It was discovered in 1934; waterflooding began in 1963. The field was developed initially on 40-acre spacing and subsequently drilled to 20-acre spacing after the start of waterflooding. The flooding pattern was first peripheral, then a three-to-one line drive, and finally an inverted nine-spot that proved most efficient for this reservoir. Reservoir characteristics are porosity of 9%, permeability of 20 md, Swi of 35%, Tr of 95°F, net-to-gross ratio of 0.18, oil gravity of 29°API, and μoi of 6 cp.

An oil viscosity of 6 cp makes the waterflood mobility ratio relatively high. From pressure cores and laboratory corefloods, waterflood residual oil saturation was estimated to be 34% of pore volume. A CO2 miscible project was evaluated with laboratory investigations, field pilots, and reservoir simulations. The pilot tests indicated that CO2 could successfully mobilize the waterflood residual oil. Even though it is difficult to determine the governing mechanisms for improved oil recovery, it appears that after the initial direct displacement of oil by the solvent bank, lighter components of the remaining oil are recovered by extraction.

The original CO2 project of 167 patterns on approximately 6,700 acres (which contained 82% of OOIP) was expanded to 7,830 acres as evaluation of performance indicated additional prospective areas. Factors affecting process design were the oil viscosity of 6 cp, high MMP, and low formation parting pressure that make operating pressure a critical factor. On the basis of the MMP estimation of 1,850 to 2,300 psi by slimtube experiments and the formation parting pressure of approximately 2,800 psi, a 2,000-psi operating pressure was selected.

Assessment of the economic viability of CO2 miscible flooding was based on pattern-element simulations for representative project areas that were then used in a scaleup program to forecast total project incremental recovery. A 2:1 WAG ratio and primary CO2 slug size of 0.40 HCPV were selected as optimum. Updated simulations after gaining operating experience indicated that a CO2 slug size of 0.60 HCPV was better.

Results of the infill-drilling program and CO2 flood combined for a total unit oil production increase from approximately 8,500 B/D in 1983 to approximately 16,000 B/D in 1987, as illustrated in Fig. 14.25. Much effort has been made to distinguish between the contributions of infill drilling, improved waterflooding, and miscible displacement. Originally incremental oil recovery resulting from infill drilling was projected to be 5.3% OOIP, while the incremental recovery resulting from CO2 flooding was to be 7.1% OOIP. These recovery estimates have increased over time as a result of an effective reservoir-management program. Current estimates of recovery resulting from primary and waterflooding methods exceed 30% of OOIP, and incremental recovery resulting from the miscible CO2 flood is more than 15% of the OOIP.

Utilization of new infill wells for injectors helped minimize downhole mechanical problems. A continuous injection-well profiling program is maintained for flood-management purposes. Increasing the WAG frequency minimized gas breakthrough between some WAG injectors and offsetting producers experiencing rapid gas breakthrough. While a detailed history-matching simulation of the test did not indicate solvent channeling through known, high-permeability leached layers to be a problem, all other indicators suggested otherwise. Dealing with leached pathways continues to be a challenge. History matching also indicated some loss of CO2 into the basal water zone.

Several production enhancements have improved field and miscible project performance. First, a 360-acre, nine-pattern pilot was implemented in the North Dome to evaluate the Lower San Andres (LSA) potential. Results showed that additional reserves could be captured from this deeper horizon, although produced-water volumes exceeded initial projections and limited near-term LSA development because of facility constraints. Once water-handling issues were addressed, 59 additional wells were deepened to the LSA in 1992. Performance of these wells provided more insight into factors affecting reservoir performance and resulted in the deepening of an additional 81 injectors and producers and upgrading of facilities to handle more water and gas. As of this writing, the deepening of 97 production wells to the LSA has resulted in a 75% increase in unit oil production and a significant increase in reserves.

Several different types of profile modifications were attempted throughout the 1990s. Early foam and polymer treatments were discontinued because of limited, short-term benefits. Preliminary results from a recent conformance program indicate the possibility to mechanically isolate mature intervals and redirect CO2 into oil-bearing intervals that would otherwise remain uncontacted.

The miscible-project performance is exceeding previous recovery projections. To better characterize the reservoir and improve business decisions for the asset, a detailed geologic study incorporating engineering and geologic data was used to provide the framework for 3D, three-phase reservoir simulation. Benefits of the study include increasing OOIP by 40%, identifying the potential in the residual oil zone found below the observed oil/water contact in the LSA, and gaining a better understanding of reservoir continuity using flow units identified with sequence stratigraphy.

Future possibilities for the miscible project include expanding the CO2-flood project on the basis of the geologic study, continuing the mechanical-isolation program to maximize sweep efficiency, and fine tuning other programs such as varying WAG ratios to further optimize flood performance and enhance profitability.

Denver Unit.[1][49][50][51][52] The Wasson Denver Unit CO2 flood, started in 1983, is one of the larger industry CO2 projects [28,000 acres, 2.1 BSTB (0.33 billion m3) OOIP]. No new wells were drilled initially for this project; however, there was significant reconfiguration of the inverted nine-spot patterns (20-acre well spacing) being used in the waterflood preceding miscible injection. Unit performance is shown in Fig. 14.26 for the period beginning with the waterflood through the first 19 years of miscible CO2 injection. The reservoir was depressured from 3,200 to 2,200 psi to reduce the amount of trapped CO2. Oil response occurred after approximately 6 to 8 months. Unit oil-production rates have been sustained since the start of CO2 injection as a result of response to miscible injection and to the continuing efforts of reservoir-management practices that identify more patterns to miscible flood and ways to improve volumetric sweep with well workovers and conversions. The first CO2 production occurred almost simultaneously with incremental oil production.

There are uncertainties in the continued waterflood curve because of the usual difficulties in estimating waterflood decline and additional uncertainties introduced as a result of pattern reconfiguration and other modifications that may have affected future waterflood performance as well as miscible recovery.

WAG Ratio. Different WAG ratios were implemented in different areas of the field to determine the most effective method. In the "Continuous Area," CO2 was injected continuously for approximately 7 years, and then some patterns were converted to 1:1 WAG to reduce CO2 producing rates. Oil rates were sustained after WAG started.

In the "WAG Area," HCPV injection rate was maintained at a level comparable to the Continuous Area. The WAG ratio was approximately 1:1. Incremental production response was poorer than in the Continuous Area, with a maximum of only about 17% of the waterflood oil rate at the start of CO2 injection. In addition, there was about a 30% loss of water injectivity, and injection pressures exceeded fracturing pressure on water cycles. The area was converted to a line drive in 1988.

As a result of the experience described above, a "Hybrid Process" was applied in a final area of the field to capture the early response of continuous injection and the long-term gas management of WAG. In this process, CO2 is injected continuously for 4 to 6 years, followed by 1:1 WAG, until a 60% (or larger) HCPV volume of CO2 is injected. The final phase will be continuous water injection.

The project has performed well overall. There were a few problems in the western part of the field, where the WAG process was used. Water injection at the desired rates was difficult, and solvent was lost to the gas cap in a limited portion of the reservoir. Neither of these was a complete surprise because the operator recognized both as potential problems during the design phase of the project. The slug process used in the eastern part of the field has performed well, and an increase in the CO2 slug size is being considered.

A fully compositional, fieldwide simulation model is being used to match field and individual-well performance. The simulator is then used to identify locations (which may require infill drilling or horizontal wells) for project expansion, which wells to shut in or return to production, where solvent losses are occurring, and needed changes in WAG ratios. Opportunities for infill drilling and pattern conversion were implemented and added several million barrels of recoverable oil.

The original estimated CO2 slug size of 0.4 to 0.6 HCPV has now been increased to 0.72%. The current estimated ultimate EOR is 16.7% OOIP. Continued improvements in reservoir management may improve this outlook.

Enriched-Hydrocarbon Projects

While applied less frequently, these types of projects have been very successful when adequate supplies of methane and enriching fluids are available and profitable to inject.

Prudhoe Bay Field.[20][35][38][39][40][42][68][69][70][71][72][73] The permotriassic-aged Ivishak (also known as Sadlerochit) reservoir, the largest producing horizon in the field, is a series of clastic zones ranging from near-shore marine deposits in the lower sections to sandstones and conglomeratic braided-stream deposits in the remaining, more productive units that contain most of the OOIP. The productive area is 225 square miles. Production began in 1977.

The reservoir is a structural stratigraphic trap consisting of a faulted, south- and southwestward-dipping, 1 to 2° homocline. The main and western areas contain gas caps with different gas/oil contacts (GOCs). Both have an oil/water contact (OWC). The reservoir has an average thickness of more than 500 ft (1600 m). Average rock and fluid properties include a porosity of 22%, S wi of 35%, a net-to-gross ratio of 0.87, an oil gravity of 28°API, and an oil viscosity of 0.8 cp. Permeability averages 500 md and ranges from <100 md in the western area and the deeper deltaic sands up to several darcies in some of the open-framework conglomerate deposits. While the reservoir is connected to a sizeable aquifer, rock properties degrade rapidly off structure, and the light oil column is separated from the aquifer in portions of the field by a heavy-oil tar mat.

The primary-depletion mechanisms were gravity drainage below the sizable gas cap, a very weak waterdrive, and a potential solution-gas drive. The initial spacing of 320 acres per well was quickly reduced to 160 acres per well and then further reduced to 80 acres per well to sustain production levels and contact more of the reservoir.

Waterflooding (planned as part of the original development plan) was started in conjunction with the 80-acre infill-drilling program. Source water was obtained from the Beaufort Sea. Inverted seven- and nine-spot injection patterns were used in areas of the oil column not overlain by the gas cap.

The gas-cap cycling project was begun in 1977. Gas-handling facilities subsequently were expanded three times and reached a capacity throughput of 8.0 Bcf/D by the late 1990s.

An enriched-hydrocarbon miscible (methane with propane and butane added) WAG injection project was initiated in 1983 with the Flowstation 3 Injection Project. This was expanded to additional areas of the field in 1987. Fig. 14.27 shows the type of recovery mechanisms being managed in various parts of the field.

Miscible Flood Specifics. The method for optimizing the WAG ratio has shifted with time. Initially, all patterns received a solvent injection (miscible injectant, or MI) of 1% of total pore volume per year. This scheme was changed to a tiered allocation process whereby the more efficient patterns received more MI. When implementation of most miscible-injection patterns (149) was completed, the allocation scheme was changed again to direct MI to the most-efficient patterns (on the basis of a detailed analysis of field-performance data) using a binary method. All patterns are ranked in order of efficiency (production per volume of returned MI, or RMI), and solvent is allocated on a WAG ratio of 1 based on a pattern’s throughput until available MI is assigned. The less-efficient patterns are shut in until another reallocation is made. A workover to correct well problems such as flow behind pipe or to open new zones or decline in another pattern’s performance can result in a shut-in pattern being ranked higher and placed on the active list. Gas samples are routinely collected from production wells to measure RMI as part of the MI allocation process.

Estimated ultimate recovery in the main part of the field is more than 60% OOIP and 80% original condensate in place. Of the total oil recovery, the miscible contribution is 10% OOIP in affected areas. Because the miscible project was started early in field history, a primary-waterflood base production curve was not established to provide an estimate of incremental recovery because of miscible injection. The miscible contribution is based on saturation changes measured by logs run in observation wells and simulations that match actual performance. Also, both tracer and log-inject-log tests have been conducted, and specialized core data have been obtained to measure and improve the effectiveness of the WAG miscible project.

Waterflood and WAG pattern recoveries can be improved with more focus on the management of individual injector/producer pairs within the floods. The objective is to ensure better vertical and areal distribution of the injectant. Patterns with the best performance have recovered in excess of 70% of the OOIP, while the poorer patterns have recovered less than 50% OOIP.

Miscible Injectant Stimulation Treatment (MIST) Concept.[72] As would be anticipated in a high-quality sand with good vertical permeability, the miscible-recovery process has been dominated by gravity forces. Rapid gravity segregation of MI away from the injection well prevents the MI from contacting a significant portion of the target waterflood residual oil, as illustrated in Fig. 14.28. As shown on the right, only a small portion of the reservoir is contacted in clean sand intervals typical of much of the miscible flood area. As shown on the left, shale lenses tend to mitigate gravity override, resulting in more of the reservoir being contacted. Both vertical and lateral MIST processes are being implemented to improve volumetric sweep. A vertical MIST process involves completing a production well at the bottom of a thick, continuous, watered-out interval. A large slug of solvent is injected, followed by a small slug of chase water. The solvent sweeps rock not contacted by previous solvent injection.

In the lateral MIST process, horizontal wells are placed in the bottom of the overridden portion of the oil column to distribute injectant laterally (Fig. 14.29). A series of injection cycles (referred to as MI bulbs) can be scheduled along the horizontal wellbore to sweep more of the oil column in the pattern. After an MI bulb is complete, the perforations are isolated with a packer or sand, and new perforations are added for the next bulb.

Fig. 14.29 shows the production history of one producer in a lateral MIST pattern. There are distinctly recognizable production increases because of miscible injection. The first response was injection in MIST injector 9-31C, where production doubled above the base rate. The second response was injection in conventional WAG injector 9-39.

The correlation between response and RMI is consistent with compositional-simulation runs that show that the rapid response is caused by vapor-phase transport. The more subdued response that occurs when liquid oil is displaced and banked by MI is more difficult to discern from other factors affecting producing rate.

Piercement Salt Dome Field. This field is composed of upper- and lower-faulted, unconsolidated sands that dip away from the salt dome at 65 to 85°. Porosity averages 26.5%, and permeability is 1.3 darcies. Oil gravity is 38°API.

This project is operated as a gravity-stable hydrocarbon miscible flood. The injection rate corresponds to a velocity of roughly one-half the critical velocity required for gravity-stable operations. A volume, corresponding to 17% PV, of enriched gas [natural-gas liquids (NGLs) plus solution gas] was injected, followed by injection of solution gas alone. When injection is completed, blowdown of the gas cap is expected to recover approximately 90% of the enriched gas and a significant portion of the injected solution gas, thus reducing the effective cost of the solvent.

Constant pressure is being maintained to improve recovery by eliminating shrinkage of oil over the course of displacement. Coreflood experiments gave recoveries similar to those predicted by the simulations. Miscible residual oil saturation in corefloods was 7% of PV. Slimtube experiments also were carried out to determine the MME required to achieve miscibility at a given pressure level. An MMP was then selected consistent with the volume of enriching NGL available for the project.

Primary production occurred through gas-cap expansion. Miscible-gas injection began after a short primary-production period. Estimated ultimate recovery is 50% of OOIP for primary recovery in both sands, 74% for total recovery after miscible flood in the lower sand, and 86% total recovery after miscible flood in the upper sand. These recovery levels were determined by tracking gas fronts with pulsed neutron capture (PNC) logging and performing material-balance calculations. These recovery levels are consistent with predictions based on simulations. To date, conformance has been excellent, with field recoveries quite similar to those seen in corefloods.

Routine pressure measurements, PNC logs, and pressure-transient testing are used to monitor reservoir performance and contact movements and to identify areas of good and poor communication. Pressure was initially allowed to decline to slightly above the MMP and was then maintained by scheduling injection volumes equal to production. Pressure maintenance became a challenge because of increasing gas/oil ratio (GOR) that resulted in water-cut increases and reservoir pressures below the MMP in some areas. Pressures were increased and maintained by curtailing production from high-GOR wells.

Pressure communication between injectors and producers has been good in the upper reservoir. In the lower reservoir, pressure communication between wells has been sporadic because of faults and shale barriers that act as baffles between injectors and producers.

Nitrogen Projects

Jay Field.[74][75] Discovered in 1970, this field produces primarily from a Jurassic-aged Smackover carbonate that is heavily dolomitized and has complex lithology. The entire pay interval was cored in virtually all the wells to provide an accurate geologic description and aid in unitization efforts. The productive area is 14,415 acres, average depth is 15,400 ft subsea (SS), porosity is 14%, permeability is 35 md, Swi is 12.7%, oil gravity is 51°API, oil viscosity is 0.18 cp, Pri is 7,850 psi, Tr is 285°F, and net-to-gross ratio is 0.27. The dip is 3°.

The field was unitized in 1973, and waterflooding began 4 days later to arrest pressure decline. Miscible N2 injection was started in 1981. The MMP for many solvents and the light oil in this reservoir is well below 7,000 psi. N2 was selected over methane and CO2 because of cost and supply considerations. Delay in methane sales was unattractive. Using CO2 would have required a long pipeline from central Mississippi with accompanying costs and right-of-way complications.

The field was developed initially with 89 wells on 160-acre spacing. Selective infill drilling later in poorer sections of the reservoir (both areally and vertically) improved the sweep of injected water. This reduced the average spacing to 140 acres per well.

The waterflood was implemented with a 3-to-1 line-drive pattern using low-salinity water from a water-source well. As-produced water is also injected. Peak water-injection rates reached 250,000 B/D.

Nitrogen is produced by three air-separation units. Produced nitrogen is recovered with cryogenic units and reinjected. The injection rate peaked at 86 MMcf/D. WAG injection is used; the WAG ratio varies by pattern, as dictated by ongoing surveillance of producer oil rate and GOR performance. Current plans call for the injection of a 0.4 HCPV bank.

The oil-production rate reached 100,000 BOPD in 1973 and was sustained at or above that level through 1979. Currently, the field is producing 10,500 BOPD at a 95% water cut. The gas production of 80 Mcf/D is approximately 75% N2. Reservoir pressure has been maintained at 7,500 psi. At this pressure, even high-water-cut wells continue to flow, negating the need for artificial lift.

Estimated ultimate recovery is 60% of OOIP, with recoveries approaching 70% in the upper section of the reservoir. Primary recovery resulting from fluid expansion and solution-gas drive was projected to be 17% of OOIP. Waterflooding increased this by 60%, and the miscible project will add another 7 to 10% of OOIP, with recovery in the upper part of the reservoir approaching 13%.

The reservoir surveillance program includes monitoring the WAG ratios, injection-to-withdrawal ratios, and profitability of each pattern. Tools used in the surveillance processes include a history-matched, fully compositional fieldwide simulation model based on an updated geologic model that includes sequence stratigraphy, geostatistical methods, and a styolite model. This finely gridded model includes an updated fluid characterization. These refinements enabled well-by-well matches of production and pressure data for all 137 wells in the field. The updated model has been used to optimize N2 distribution and investigate several operational changes that are scheduled for implementation and will extend field life by more than 10 years.

Overall Industry Experience

Fig. 14.30 shows how incremental ultimate recovery increased with total solvent slug size for a few projects for which these data were available. The interpretations given in this figure are the author’s and are not necessarily those of the project operators. Many of the projects represented in this figure are ongoing, and the ultimate incremental recovery is an estimate.

The contribution of incremental oil production from gas-injection projects (mostly miscible) has continued to grow since 1984, as reported biennially by the Oil & Gas Journal. Fig. 14.31 shows the volumes reported in this reference. In 2002, total incremental production was nearly 550,000 BOPD. Approximately 60% of the total comes from hydrocarbon miscible projects, and most of the remainder comes from CO2 miscible projects in the U.S. The advent of the significant contributions from the projects (hydrocarbon miscible) in Venezuela beginning in 2000 indicates an ongoing effort to identify and implement miscible projects around the world.


Miscible injection has been applied successfully in many reservoirs. The resulting experience has made it possible to reliably predict the economic viability of new projects in other reservoirs. This chapter contains some general guidelines that should suffice in screening studies of the applicability of a miscible process to a given reservoir or field. In addition, more-detailed information on phase behavior and compositional simulation has been discussed to provide guidance on how to make a more-detailed assessment of miscible flooding. Several field examples are presented to illustrate how CO2, enriched hydrocarbons, and N2 solvents have been used to increase oil recoveries significantly. Finally, proper assessment of the application of a miscible project should include the timing of capital outlays for project implementation, the timing of solvent injection and production response, changes in injectivity, and the costs and need to reinject produced solvent.


a = constant
A = defined with Eq. 14.8
b = constant
B = defined with Eq. 14.9
C = defined with Eq. 14.11
D = adsorbed concentration
f = fugacity
RTENOTITLE = fugacity of component j in the liquid phase, psi
RTENOTITLE = fugacity of component j in the vapor phase, psi
F = flowing concentration
g = gas
i = component i
j = component j
k = permeability, md
n = number of components
Nca = capillary number, dimensionless
Δpg = pressure gradient through the displacing phase, psi
pc = component critical pressure, psia
P = pressure, psia
Pri = initial reservoir pressure, psi
R = universal gas constant, units consistent with other equation parameters
s = volumetric shift parameter, dimensionless
S = saturation, fraction of pore volume
RTENOTITLE = maximum solvent saturation, fraction of pore volume
RTENOTITLE = solvent trapped when rock is 100% solvent saturated
Sgt = trapped gas saturation, fraction of pore volume
Soi = initial oil saturation, fraction of pore volume
Sorm = miscible flood residual oil saturation, fraction of pore volume
Sorwf = final waterflood residual oil saturation, fraction of pore volume
Swi = initial water saturation, fraction of pore volume
t = trapped
T = temperature, ° R
Tc = critical temperature, ° R
Tr = reduced temperature, T/Tc
v = volume, cubic ft
vxs = fluid velocity along a streamline, ft/sec
x = distance along the streamline, ft
x,y,z = simulator grid directions
Z = fluid compressibility factor, dimensionless
ω = component acentric factor, dimensionless
ϕ = accessible pore volume
κ = define by Eq. 14.6
σ = interfacial tension, dynes/cm
ij = binary interaction coefficient
μoi = oil viscosity, cp
Ωa = constant in Eq. 14.3
Ωb = constant in Eq. 14.5


The authors would like to express their appreciation to members of ExxonMobil Production Co. for their help in describing the performance of the Means San Andres Unit miscible flood performance and the members of Oxy Petroleum for their help in describing the Denver Unit miscible flood performance.


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SI Metric Conversion Factors

acre × 4.046 873 E + 03 = m2
°API 141.5/(131.5 + °API) = g/cm3
bbl × 1.589 873 E − 01 = m3
cp × 1.0* E − 03 = Pa•s
dyne × 1.0* E − 02 = mN
ft × 3.048* E − 01 = m
ft3 × 2.831 685 E − 02 = m3
°F (°F − 32)/1.8 = °C
in. × 2.54* E + 00 = cm
psi × 6.894 757 E + 00 = kPa


Conversion factor is exact.