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Production forecasting system constraints

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Hydrocarbon production potential is often limited by constraints, and it is important that these constraints are understood and correctly represented when generating a realistic set of production profiles. The focus of this section is physical constraints in the system through which the fluid flows, but constraints applied because of reservoir management, contractual terms and economics are also highlighted.

Choke modeling

A production system includes the reservoir, wells, facilities and export system. Constraints within the system can be associated with any of the produced fluids (oil, gas or water) or a specific combination of them. For example, important factors to consider beyond the base deliverability of the reservoir are potential near-wellbore formation damage (skin), well tubing constraints, artificial lift availability, shared gathering system back pressures, flow line erosion velocity limits and facility capacities.

It is useful to define a model for a production system to fully understand production potential and constraints. Such a model is described in J.A.Spencer's Application of Forcasting and Uncertainty Methods to Production.[1]

INSERT Figure 1 – Choke model (Pending permission approval)

Fig 1 shows only four chokes, as a minimum, but these can be further subdivided; for example, splitting out a gathering system between wells and facilities. This model should record the current potential, production and therefore “efficiency” (often referred to as availability or uptime fraction) of each part of the system. For example, the reservoir penetrated in a particular well may have the potential to deliver 10,000 BOPD but the well has been choked back to 6,000 BOPD to comply with a drawdown limit being imposed because of a perceived sand production risk. This well would be said to be producing at 60% efficiency. In this example, there is a good reason for the constraint being imposed but it is important for the integrated asset team to be aware of it and the reasons behind it. The choke model allows an integrated asset team to focus on the segments of the production system that are constraining production potential (low efficiency) and that could potentially be improved to increase production either with a change in operating conditions or additional operating cost or capital expenditure. This process is often called “production optimization” and “debottlenecking”.

It is important to remember that a choke model as described here represents a snapshot of the asset at any given time and will need to be regularly updated. Increases in water cut, GOR, etc. all have an impact on the efficiencies of the subsystems in the model. A choke model is therefore not a forecasting tool in itself; however, it can be used to understand the range of current potential and the associated constraints of the integrated system to ensure realistic short-term forecasting and also to test potential “what if” scenarios.

Integrated production system modeling

The concept of understanding system constraints and production optimization, especially in terms of future changes, is taken a step further when an asset team decides to build a fully Integrated Production System Model (IPSM). This is a dynamic subsurface/surface-linked simulation that will model the flow of fluid and change in composition through the entire production system. These models can be used to better understand potential and constraints of the system at a specific point in time, or they can be run out over time to generate production profiles under a particular development concept. These models can be complex and slow to run, depending on the detail included, so clear objectives need to be set up front to ensure the appropriate level of fidelity is used to model each part of the system. Building an IPSM requires very effective collaboration between the reservoir, production, subsea and facilities engineers, plus significant time and resources, but for complex systems can be considered best practice for understanding the asset and developing realistic production profiles that take into account all the constraints in the system. If properly constructed, an IPSM could be used by all the aforementioned disciplines to address the whole spectrum of production forecasting objectives, from short-term operational forecasting and production optimization to development planning forecasts through to resource estimation (see also Production forecasting frequently asked questions and examples - Example 5).

References 2-4 are example case studies of the use of IPSMs across the industry.

System availability and deferment

As already discussed, it is important that production profiles take into account any expected production losses, known as deferment, both planned and unplanned, through what is known as “availability” (sometimes referred to as uptime or operating efficiency), i.e., actual production/integrated production system capacity (IPSC).

Reasons for planned production deferment include scheduled maintenance, well surveillance or facility/export system upgrades that result in additional constraints, or complete production system shut downs for a specified period of time. Reasons for unplanned deferment include system trips or restrictions due to faulty equipment, unstable production, weather or human error. Operational personnel often refer to the reliability of a production system, which excludes the planned deferment of the system, i.e., (IPSC-Unplanned deferment)/IPSC.

To incorporate system availability into production profiles, it is important that cross-discipline discussion occurs between subsurface, production and operations. It is important that the scope and impact of planned deferment is well understood between parties. It is also important that reasons for unplanned deferment are recorded and discussed as part of choke modeling to try to minimize these losses going forward and to provide a statistical basis for predicting future system availability.

Unplanned deferment forecast should be consistent with historical trends unless there is a good reason to differ from this (e.g., investment is being made to improve reliability). For weather related downtime (eg. Hurricane season in Gulf of Mexico, freeze-offs in Canada), this is likely to vary through the year but historical trends on this should be well known. For equipment failure analogue data can be used, eg. if analog performance indicates that well require a pump change on average every 1.5 years, this could be scheduled as deferment even though the actual date of this occurrence is unknown. Or if corrosion is expected to produce a hole in the production string every 10 years, the same method can be applied. Other types of failures that could be forecasted in this manner include: wax restriction / hot oil jobs, rod breaks, pump failures, plunger maintenance, etc. For longer-term forecasts, the aging of facilities should be recognized in the prediction of overall reliability and planned deferment; without significant continued maintenance investment the reliability of facilities normally reduces over time.

System availability is normally modeled as an average annual factor in the out years, but should be modeled explicitly for the first one to two years based on scheduled maintenance and the reliability factor.

Secondary fluid production, Injection and emissions

Production of the primary fluid (oil, gas or gas condensate) is often constrained by the byproducts (water and often gas) exceeding facility capacity, so it is important to spend sufficient time and effort developing realistic forecasts of water and gas even if they are non-revenue generating.

First water breakthrough in a field usually results in the oil rate coming off plateau and a new production regime as water handling capacity is tested and new issues arise in the field (e.g., sand production, corrosion, H2S) that can also affect availability. There is often uncertainty about the expected timing of water breakthrough, and this should be accounted for in the range of production profiles developed. An increase in water and/or gas production can lead to producers being shut-in due to lack of water handling or gas compression capacity. Well production prioritization then needs to be considered until constraints can be lifted.

For forecasts generated using simulation models, constraints can be applied by phase for various stages of the facility, and prioritization criteria can be input to dictate which wells are produced when constraints are exceeded. For forecasts generated by decline curve or other methods, special attention should be paid to the secondary phases to understand potential constraints.

Injection capacity limits also need to be taken into account. These may affect production if, for example, voidage replacement is required to keep the reservoir above the bubblepoint. Injection forecasts can be done effectively using material balance and simulation models in terms of total volumes needed for pressure support. A well history matched finite difference simulation model is the best tool to predict how the reservoir will be swept going forward and to optimize spatial water or gas injection; however, other tools, such as streamline modelling, can be used in conjunction with tracer injection and interference testing in the field to better understand injector/producer interactions and improve reservoir management and associated injection planning going forward.

Emissions forecasts, specifically CO2 into the air and water disposal, are needed to quantify the impact of operations on the environment. Actual emission compliance requirements are country specific and it is important to be familiar with what these are for your area of operation.

Reservoir management constraints

Oil and gas field development and production is usually optimized to maximize economic return; however, sustaining production and maximizing recovery factor, and therefore reserves, are also important factors, for certain stakeholders more than others, and these need to be considered in production forecasting. Governments are usually a major stakeholder in any oil and gas development, and in some countries, will set depletion policies that limit individual well or overall field rates to sustain production, maximize recovery and/or regulate the income received from the industry. OPEC will set production constraints in its member countries

“to secure fair and stable prices for petroleum producers; an efficient, economic and regular supply of petroleum to consuming nations; and a fair return on capital to those investing in the industry.”

Operating companies and the teams within them that manage producing assets usually maintain “reservoir management guidelines” to direct how a reservoir is produced. Different development and production scenarios will be reviewed to assess the impact on the ultimate recovery of the field. Often lower production rates result in higher ultimate recovery (but lower NPV), so there is a balance that needs to be understood and production constraints set if necessary. Another example is where there is a prolonged downtime in the injection system during a water or gas flood that will result in reduced ultimate recovery if production wells are not also constrained.

Contractual constraints

Oil and gas assets often have one or more commercial contracts in place that can affect how an asset is produced. The most important of these contracts is any sales agreement that is in place with a customer for the oil or gas production. Sales agreements are more common for gas fields, and in these cases, the production forecast is likely to depend heavily on customer requirements. The sales agreement will usually call for a long-term plateau rate but, within this, ACQ (annual contract quantity) and DCQ (daily contract quantity) may vary. Depending on the customer, public holidays and seasonal weather may affect demand and therefore customer nominations across the year. This continuity of demand will be represented in a contract by a “load factor” (the ratio of the average load vs. the peak load). Contracts will also include clauses that cover the penalties incurred if there is a shortfall in delivery (likewise “take or pay” conditions for the customer if they do not take delivery of the gas), and these should be considered in overall field economics and may dictate how the field is developed, how much excess production potential is put in place and therefore the expected production profile. Production forecasting will require proper balancing of system availability with contractual load factor. Fig 2 illustrates some of these gas contract terms.

INSERT Figure 2 Gas contractual constraints (Pending permission approval)

Where facilities and pipelines are shared there will often be a PHA (production handling agreement) in place with specifications of how capacity is allocated between assets, and this may limit off take.

PSCs (production sharing contracts) will specify how fiscal terms will change over time (usually against cumulative production), which is likely to impact the development plan and will definitely impact the net production profile.

Economic constraints

It is important to assess at what point in time long-term production profiles become uneconomic (i.e., operating costs exceed the revenue generated by production) so that reserves and abandonment timing are properly captured. As well as optimizing the future development plan of an asset, this requires a discussion with operations on how operating costs are likely to change in the future with production. It is also important to understand the expected production profiles from other assets that may share the production facilities and therefore operating costs.


  1. Spencer, J. A., & Morgan, D. T. K. (1998, January 1). Application of Forecasting and Uncertainty Methods to Production. Society of Petroleum Engineers.

Noteworthy papers in OnePetro

Jalilova, N., Tautiyev, A., Forcadell, J., Rodriguez, J. C., & Sama, S. 2008. Production Optimization in an Oil Producing Asset - The BP Azeri Field Optimizer Case. Society of Petroleum Engineers.

Okoh, E., Sathyamoorthy, S., Olaniyan, E., & Ezeokeke, O. 2010. Application of Integrated Production System Modelling in Effective Well and Reservoir Management of the Bonga Field. Society of Petroleum Engineers.

Roadifer, R. D., Sauve, R. E., Torrens, R., Mead, H. W., Pysz, N. P., Uldrich, D. O., & Eiben, T. 2012. Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska. Society of Petroleum Engineers.

Noteworthy books

Society of Petroleum Engineers (U.S.). 2011. Production forecasting. Richardson, Tex: Society of Petroleum Engineers. WorldCat or SPE Bookstore

External links

Production forecasts and reserves estimates in unconventional resources. Society of Petroleum Engineers.

Production Forecasts and Reserves Estimates in Unconventional Resources. Society of Petroleum Engineers.

See also

Production forecasting glossary

Aggregation of forecasts

Challenging the current barriers to forecast improvement

Commercial and economic assumptions in production forecasting

Controllable verses non controllable forecast factors

Discounting and risking in production forecasting

Documentation and reporting in production forecasting

Empirical methods in production forecasting

Establishing input for production forecasting

Integrated asset modelling in production forecasting

Long term verses short term production forecast

Look backs and forecast verification

Material balance models in production forecasting

Probabilistic verses deterministic in production forecasting

Production forecasting activity scheduling

Production forecasting analog methods

Production forecasting building blocks

Production forecasting decline curve analysis

Production forecasting expectations

Production forecasting flowchart

Production forecasting frequently asked questions and examples

Production forecasting in the financial markets

Production forecasting principles and definition

Production forecasting purpose

Production forecasting system constraints

Quality assurance in forecast

Reservoir simulation models in production forecasting

Types of decline analysis in production forecasting

Uncertainty analysis in creating production forecast

Uncertainty range in production forecasting

Using multiple methodologies in production forecasting