Development of an Intelligent Systems Approach for Restimulation Candidate Selection

Development Of An Intelligent Systems Approach For Restimulation Candidate Selection Shahab Mohaghegh; Shahab Mohaghegh West Virginia University Search for other works by this author on: This Site Google Scholar Scott Reeves; Scott Reeves Advanced Resources International Search for other works by this author on: This Site Google Scholar David Hill David Hill Gas Research Institute Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/CERI Gas Technology Symposium, Calgary, Alberta, Canada, April 2000. Paper Number: SPE-59767-MS https://doi.org/10.2118/59767-MS Published: April 03 2000 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Get Permissions Search Site Citation Mohaghegh, Shahab, Reeves, Scott, and David Hill. "Development Of An Intelligent Systems Approach For Restimulation Candidate Selection." Paper presented at the SPE/CERI Gas Technology Symposium, Calgary, Alberta, Canada, April 2000. doi: https://doi.org/10.2118/59767-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Unconventional Resources Conference / Gas Technology Symposium Search Advanced Search AbstractThis paper summarizes the development of a methodology for the restimulation candidate selection in tight gas sands. The methodology incorporates virtual intelligence techniques (artificial neural networks, genetic algorithms and fuzzy logic) to achieve this objective. Artificial neural networks are used to develop a representative model of the completion and hydraulic fracturing process in a specific field. Genetic algorithms are used as a search and optimization tool to identify the missed incremental production based on the neural network model. Finally fuzzy logic is used to capture the unique field experiences of the engineers as well as detrimental parameters (if such parameters are indeed present) and incorporate them in the decision making process. Approximate reasoning approach is used at the decision making level to identify the restimulation candidates. Once the methodology is introduced, it is applied to an actual tight sand field in the Rocky Mountain region and the results are presented.Statement of the ProblemIn 1996, the Gas Research Institute (GRI) performed a scoping study to investigate the potential for natural gas production enhancement via restimulation in the United States (lower 48 onshore). The results indicated that the potential was substantial (over a Tcf in five years). Particularly in tight sand formations of the Rocky Mountains, Mid-Continent and South Texas regions. However, it was also determined that industry's current experience with restimulation is mixed, and that considerable effort is required in candidate selection, problem diagnosis, and treatment selection/design/ implementation for a restimulation program to be successful. Given a lack of both specialized (restimulation) technology and "spare" engineering manpower to focus on restimulation, GRI initiated a subsequent R&D project in 1998 with several objectives. Those objectives are todevelop efficient, cost-effective, reliable methodologies to identify wells with high restimulation potential,identify and investigate various mechanisms leading to well underperformance, anddevelop and test restimulation techniques tailored to each cause of to well underperformance1.Addressing the first of the project objectives, an integrated methodology has been developed to select high-potential restimulation candidates in a reliable, cost-effective manner. The technique involves several steps. First, sophisticated statistical approaches are utilized to identify both obvious and subtle differences in well performances, and provide initial insights into potential candidate wells. Secondly, virtual intelligence techniques (a hybrid of artificial neural networks, genetic algorithms, and fuzzy logic) are used to recognize patterns in well performances as they relate to both geologic/reservoir conditions and completion/stimulation operations. With this information, controllable well performance "drivers" can be identified, and this information can in turn be used to select candidate wells, identify possible causes of well underperformance, and begin the treatment selection process. Third, engineering methods such as type-curves are used to high-grade potential restimulation candidates by providing a (relative) indication of reservoir quality and completion efficiency, and hence restimulation potential. Finally, high-potential candidates are individually screened for mechanical integrity, reservoir pressure and other important historical information that may not be uncovered in the previous steps. Lastly, low-cost candidate verification tests are performed to ensure candidate selection potential. Keywords: artificial intelligence, frontier formation, reservoir quality, category, neural network, algorithm, information, intelligent system approach, mohaghegh, machine learning Subjects: Information Management and Systems, Artificial intelligence This content is only available via PDF. 2000. Society of Petroleum Engineers You can access this article if you purchase or spend a download.

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