Energy Policy Act of 2005 (Ultra-deepwater and Unconventional Resources Program)
Optimizing Development Strategies To Increase Reserves In Unconventional Gas Reservoirs
The goal of this project is to develop new reservoir and decision modeling tools to help operators determine optimal well spacing and completion strategies in unconventional gas reservoirs. These tools should help operators maximize revenue and profitability margins in tight gas reservoir development areas. The new modeling tools will be applied to the Barnett Shale play in Parker County, Texas, and to deep basin tight gas sands in the Berland River area of Alberta, Canada.
Texas Engineering Experiment Station, College Station, TX 77843
The University of Texas at Austin, Austin, TX 78712
Unconventional Gas Resources Canada Operating, Inc. (UGR), Calgary, Alberta, Canada
Pioneer Natural Resources Company, Irving, TX 75039
Most state-of-the-art reservoir modeling approaches require a comprehensive, integrated evaluation of large volumes of geological, geophysical, petrophysical, and engineering data. First, a geological model is built to characterize the estimated distributions of reservoir properties such as porosity and permeability, and then reservoir simulation runs are carried out to predict and optimize the performance of new wells. Unfortunately, such in-depth modeling is time-consuming and expensive, so it is not routinely employed by smaller operators. In addition, these models are generally deterministic and produce a single realization of the reservoir, despite the fact operators face significant uncertainty. This project aims to develop practical reservoir and decision modeling tools that can be utilized by smaller operators who are looking for quick recommendations for optimizing well spacing and completion strategies in tight gas reservoirs.
The reservoir model developed in this project will likely be based on either fast reservoir simulation modeling or “moving window” methods, which involve local analyses in an areal window centered around an existing well. A model-based linear 4D regression equation is applied within each window, and once the regression coefficients are determined, performance can be estimated for infill wells by substituting appropriate values for infill well conditions. This method is fast and relies only upon well location and production data. This project aims to combine a fast, probabilistic reservoir model with a Bayesian decision model for selecting primary and secondary development strategies.
The first part of the project will focus on the development of the reservoir model, and the second part will focus on integrating the reservoir model with the decision model and applying the integrated development model to actual unconventional gas reservoirs in Texas and Alberta, Canada. The reservoir and decision model results will be used by the project’s industry partners to design development plans for these reservoirs.
The project will be executed by a team of researchers from Texas A&M University and The University of Texas at Austin, with technical and cost-sharing support provided by UGR and Pioneer, the operators of the reservoirs designated for testing the combined reservoir and decision model.
Deliverables for this project will include: 1) a preliminary version of the reservoir model developed under this project; 2) a preliminary version of the Bayesian decision model developed under this project; 3) an integrated reservoir and decision model to be delivered as software that operates in a readily accessible environment; and 4) reservoir characterization reports and recommendations for optimal spacing and completion strategies for the two test reservoirs, the Barnett Shale in Parker County, Texas, and the Gethring Formation of the Berland River area in Alberta, Canada.
This project is expected to result in reservoir and decision modeling tools that will help operators increase reserves and accelerate production of unconventional gas resources throughout the U.S.
In the near term, these reservoir and decision modeling tools will be used to make specific well spacing and well completion recommendations to TAMU’s industry partners. It is anticipated that Pioneer will implement these recommendations in their Barnett Shale development areas, and UGR will implement project recommendations in their deep basin tight gas play in western Canada.
Over the long term, the project’s reservoir and decision modeling tools may be adopted by additional operators as a quick way to determine optimal well spacing and completion strategies early in the lives of unconventional reservoirs. Such a tool would allow operators to minimize the number of sub-economic wells that are drilled, which not only conserves capital but also reduces the environmental impacts of drilling.
If successful, this project could lead to improved drilling practices in tight gas basins throughout the United States. According to the National Petroleum Council (2007), North America has over 5000 TCF of natural gas resources in shale or tight sand formations. In the lower 48 states, nearly 300 TCF of this is estimated to be technically recoverable. In specific development areas, such as the Barnett Shale, current recovery per well averages just 7% of gas in place, which is far below the 20% that many believe is achievable. The research proposed here would lead to more efficient development of unconventional gas reservoirs and could potentially increase the amount of this gas that is brought to market. Increased domestic gas production would result in increased tax revenues, royalties, and regional economic benefits.
The team developed and tested initial reservoir and decision models of the Gething formation. The reservoir characterization included porosity, permeability, net pay and drainage area as random variables, and included correlation of permeability with porosity. They adjusted permeability and well completion data to calibrate the Gething model against actual production data.
The team incorporated a geostatistical analysis based on a Schlumberger reservoir study and updated dependencies in reservoir properties. Spatial variations of reservoir properties in the Gething reservoir were considered to determine dependencies in production response between wells for decision modeling. They then re-ran the probabilistic reservoir model with updated reservoir property correlations. In consultation with Unconventional Gas Resources, the team constructed, tested and ran a preliminary probabilistic reservoir model of the Gething tight gas formation. The team revised the reservoir model to run to economic limit and calculated discounted production, so decisions are based on upon discounted EUR rather than stage production.
The team has begun work on a decline curve based model that can be fully integrated into the decision model.
The team has developed a preliminary decision model that allows for two development stages: a primary development stage and one stage of downspacing. The model incorporates uncertainty in production, modeled via decline curves, and chooses the optimal development plan. This model integrates the reservoir model via a fitting procedure.
The key tasks to be undertaken detailed in the Project Management plan are outlined below.
Develop and Validate Fast Reservoir Model. The project team will continue to develop a fast, approximate, probabilistic reservoir model for predicting well performance in unconventional gas reservoirs. The model will combine aspects of statistical moving window methods and simulation-based approaches, and will be able to calculate input parameters, or proxies for these parameters, using readily available data such as production data. Also, the model will be capable of incorporating additional data, such as well log or pressure transient test data, when those data are available. Another important characteristic of the model will be its ability to predict reservoir performance under different development scenarios with a statistical quantification of uncertainty. Predictions of future reservoir performance will be presented in the form of probability distributions. The model will be validated using synthetic data sets.
Develop Flexible Decision Model. The project team will develop a Bayesian decision model that will allow operators to choose optimal primary and secondary development plans in unconventional gas reservoirs. The decision model will consider the optimal primary well spacing and completion method and allow for down-spacing at a later date. In addition, the model will allow operators to choose from among a selection of specific objectives, such as “maximizing profitability” or “maximizing ultimate recovery.” The decision model will be developed in Excel, Visual Basic, or CPLEX. The project team will work closely with the industry partners to make certain that the structure and assumptions of the decision model are practical from the perspective of an operator. The model will be tested on simple development problems before being extended to actual development decisions.
Integrate Reservoir and Decision Models. The project team will integrate the decision model with the reservoir model. TEES will test the integrated reservoir and decision models on synthetic data sets, both to verify the reliability of the models and to ensure that the models can be run in practical time frames.
Apply Reservoir and Decision Models in Two Unconventional Reservoirs. The researchers will apply the integrated reservoir and decision model in two different unconventional reservoirs, working closely with the operators to model actual development and pilot program decisions. TEES will characterize the reservoirs for the purpose of compiling the input data required to run the models and will also determine correlation and dependencies between parameters used in modeling. The researchers will also work with the participating companies to benchmark and summarize the success of their pilot programs in unconventional reservoirs. The two unconventional reservoirs to be studied are: the Barnett Shale in Parker County, Texas (with Pioneer), part of the established Fort Worth Barnett Shale play, and the Gethring Formation, in the Berland River Area of Alberta (with Unconventional Gas Resources), a tight gas sand in the Deep Basin tight gas play of western Canada.
Technology Transfer. TEES will work closely with the industry partners and RPSEA to develop a program to transfer the results of this research. Technology transfer will include industry workshops, SPE papers and presentations, and presentations to the members of the Center for Unconventional Gas Resources in the Crisman Institute for Petroleum Research at Texas A&M University. This group includes many operators active in unconventional gas exploration and development and meets approximately twice per year.
Project Start: August 26, 2008
Project End: August 31, 2011
DOE Contribution: $314,606
Performer Contribution: $80,000
RPSEA – Charlotte Schroeder (firstname.lastname@example.org or 281-690-5506)
NETL – Virginia Weyland (Virginia.Weyland@netl.doe.gov or 281-494-2517)
TEES – Duane McVay (email@example.com or 979-862-8466)
UT – Eric Bickel (firstname.lastname@example.org or 512-232-8316)
Final Project Report [PDF-4.95]