07122-35

Primary Performer
Texas Engineering Experiment Station, College Station, TX 77843

Additional Participants
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

Abstract

Objective:
The goal of the proposed project is to develop integrated reservoir and decision models to help operators in unconventional gas reservoirs increase reserves and accelerate production, while protecting the environment, by determining the optimal well spacing and completion strategy as quickly as possible. This goal requires explicit modeling of subsurface uncertainty and advanced decision tools that take this uncertainty into account to optimally manage risk. These tools and methods will be developed within the context of existing and emerging gas shale and tight gas reservoirs.

Project Description:
According to the National Petroleum Council, North America has over 5000 trillion cubic feet (TCF) of natural gas resources in shale or tight sand formations. While this resource base is large, developing it in an economic and environmentally sensitive manner is challenging. For example, in the lower 48 United States, around 300 TCF of the shale gas and tight gas is estimated to be recoverable using existing technology. In specific development areas such as the Barnett Shale, current recovery per well averages just 7% of gas in place --- far below the 20% that many believe is achievable. In addition to low recovery rates, operators in unconventional reservoirs must invest large amounts of capital and face significant risks.

To address this challenge, the Texas Engineering Experiment Station (PI’s Duane A.McVay and J. Eric Bickel) proposes to develop new technologies and methods for determining optimal development and testing programs, including well spacing and completion practices, in gas shale and tight sand reservoirs. The core of this technology will be an integrated reservoir and decision model that fully incorporates uncertainty. The reservoir model will be based on moving window and reservoir simulation techniques, while the decision model will employ dynamic programming to determine the optimal development and testing program.

Impact and Benefits:
This research will increase the Nation’s gas reserve base and accelerate production by enabling operators to determine optimal development plans sooner, rather than over 20-30 years as has happened historically. In addition, optimal development will minimize the number of wells required, with attendant environmental benefits. This is particularly important since some large unconventional gas reserves lay under densely populated urban areas.

Principal Investigators Duane McVay, Eric Bickel