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The resource triangle, Fig. 1, describes the distribution of original gas in place (OGIP) in a typical basin.^{[1]} At the top of the triangle are the high permeability reservoirs. These reservoirs are small, and, once discovered, as much as 80 to 90% of the OGIP can be produced using conventional drilling and completion methods. As we go deeper into the resource triangle, the permeability decreases, but the size of the resource increases. Higher gas prices and better technology are required to produce significant volumes of gas from these tight gas reservoirs.
The recovery efficiency is computed by dividing the cumulative gas produced by the OGIP volume. In a tight gas reservoir, the recovery efficiency varies from less than 10% to more than 50% of the OGIP. The recovery efficiency is a function of:
 Permeability
 Net gas pay thickness
 Drainage area
 Effective fracture halflength
 Economic limit
 Well life
Reserve evaluation methods
The most common methods used by reservoir engineers to determine reserves are :
 Volumetric
 Materials balance
 Decline curves
 Reservoir models
Table 1 presents information concerning how these methods are used to evaluate high and low permeability gas reservoirs
Volumetric method
Volumetric methods can be used to estimate reserves from high permeability, blanket, and depletion drive gas reservoirs. In such reservoirs, the drainage area and gas recovery efficiency are usually known with reasonable certainty; thus, the volumetric method can provide relatively accurate estimates of OGIP and reserves.
In tight gas reservoirs, the volumetric method might provide reasonable estimates of OGIP; however, estimates of gas reserves are not as reliable because it is very difficult to estimate both the drainage area of a given well and the recovery efficiency. Because the drainage area and recovery efficiency are so difficult to estimate in tight gas reservoirs, the volumetric method of estimating reserves should only be used prior to drilling any wells and only as a last resort. Once drilling and production data are available, production data analyses should be used to estimate reserves.
Material balance method
The material balance method should be used only in high permeability gas reservoirs when accurate gas production and reservoir pressure data are available. In high permeability gas reservoirs, the wells can be shut in for hours or days, and accurate estimates of the average reservoir pressure can be measured or computed using Horner graphs. If the high permeability reservoir is connected to a strong aquifer, or the reservoir rock is very compressible, material balance methods can still be used but are less accurate because of the complexity of the problem and the difficulty in developing an accurate data set.
In tight gas reservoirs, material balance methods should never be used because it is impossible to obtain accurate data to describe how the reservoir pressure declines as gas is produced. In a tight gas reservoir, a well (or the entire reservoir) must be shut in for months or years before enough pressure data are collected to accurately estimate the average reservoir pressure. As such, virtually all shutin pressure measurements in tight gas reservoirs underestimate the value of average reservoir pressure. If the data are used, the estimate of OGIP and ultimate gas recovery will be too low.
Decline curve method
In most gas reservoirs, the decline curve analysis method can be used to estimate reserves. For high permeability reservoirs, the decline curve method works even with limited production data using the exponential equation, which is written as
where a is the (constant) instantaneous decline factor; q is the flow rate at time, t; and q_{o} is the initial flow rate. When Eq. 1 applies, a graph of gas flow rate vs. the logarithm of time is a straight line. The straight line can be extrapolated to an economic limit or a fixed well life to determine the ultimate gas recovery. Fig. 2 illustrates typical data that can be described using an exponential decline.
For tight gas reservoirs, especially layered reservoirs that have been stimulated with a large hydraulic fracture, decline curve analyses methods can be used, but a hyperbolic equation must be used to curve fit the data and to extrapolate the data to an economic limit. The hyperbolic decline equation is
where a_{o} is the initial instantaneous decline factor. The decline factor, a, decreases with time, as given by
Near the end of the life of the well, the decline becomes exponential again. Usually, if the decline rate decreases below 6 to 8%, the user sets the decline rate constant (at 6 to 8%) for the remaining life of the well. Fig. 3 illustrates a typical exponential decline for a tight gas well. This well is a Cotton Valley well in east Texas that was originally completed and fracture treated in the early 1980s in the lower Cotton Valley zone called the Taylor sand. In the early 1990s, the well was completed and fracture treated in the upper Cotton Valley. The gap in gas production data in the early 1980s was because of the gas market and curtailment of production.
Even when using the hyperbolic equation to analyze production from tight gas reservoirs, one must carefully analyze all the data. For example, many wells begin producing at high gas flow rates along with high flowing tubing pressure. During the first few weeks and months, both the gas flow rate and the flowing tubing pressure decline. If the analyst only analyzes the gas flowrate data, the extrapolation into the future is optimistic. Whenever the flowing tubing pressure reaches the pipeline pressure, and the flowing tubing pressure quits declining, the gas flowrate decline rate increases. Thus, when both the gas flow rate and the flowing tubing pressure are declining, the analyst needs to compute values of q/Δp or flow rate divided by pressure drop and use the decline curve model to match both the decline in flow rate and the decline in flowing tubing pressure.
Reservoir modeling method
The most accurate method of estimating gas reserves in tight gas reservoirs is to use a reservoir model, such as a semianalytical model or a numericalreservoir model, to history match production data from the well. The model should be capable of simulating layered reservoirs, a finite conductivity hydraulic fracture, and a changing flowing tubing pressure. In some cases, the analyst might also need to simulate nonDarcy flow, fracture closure, and/or fracture fluid cleanup effects.
Normally, a reasonable approach to estimating reserves is to use decline curves to review and qualitycheck the data; semianalytical models to history match existing data and estimate reserves; and finite difference models to analyze the data, especially if factors such as nonDarcy flow, fracture closure, and fracture fluid cleanup need to be included in the analysis. Fig. 4 illustrates how the saturation profile around a hydraulic fracture can be simulated to better understand fracture fluid cleanup and its effect on gas production vs. time.
Nomenclature
a  =  decline factor 
a_{o}  =  initial instantaneous decline factor 
e  =  exponent 
q  =  flow rate, Mcf/D 
q_{o}  =  initial flow rate 
t  =  time, hours or days 
References
 ↑ Masters, J.A. 1979. Deep Basin Gas Trap, Western Canada. AAPG Bulletin 63 (2): 152.
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See also
Permeability estimation in tight gas reservoirs
Statistical data correlations in tight gas reservoirs
Hydraulic fracturing in tight gas reservoirs
Log analyses in tight gas reservoirs
Core analyses in tight gas reservoirs
Tight gas drilling and completion