Use of genetic algorithms and neural networks to optimize well locations and reduce well requirements

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A goal common to both the environmental and petroleum industries is the reduction of costs and/or enhancement of profits by the optimal placement of extraction/production and injection wells. Formal optimization techniques facilitate this goal by searching among the potentially infinite number of possible well patterns for ones that best meet engineering and economic objectives. However, if a flow and transport model or reservoir simulator is being used to evaluate the effectiveness of each network of wells, the computational resources required to apply most optimization techniques to real field problems become prohibitively expensive. This paper describes a new approach to field-scale, ... continued below

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11 p.

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Johnson, V.M. & Rogers, L.L. September 1, 1994.

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Description

A goal common to both the environmental and petroleum industries is the reduction of costs and/or enhancement of profits by the optimal placement of extraction/production and injection wells. Formal optimization techniques facilitate this goal by searching among the potentially infinite number of possible well patterns for ones that best meet engineering and economic objectives. However, if a flow and transport model or reservoir simulator is being used to evaluate the effectiveness of each network of wells, the computational resources required to apply most optimization techniques to real field problems become prohibitively expensive. This paper describes a new approach to field-scale, nonlinear optimization of well patterns that is intended to make such searches tractable on conventional computer equipment. Artificial neural networks (ANNs) are trained to predict selected information that would normally be calculated by the simulator. The ANNs are then embedded in a variant of the genetic algorithm (GA), which drives the search for increasingly effective well patterns and uses the ANNs, rather than the original simulator, to evaluate the effectiveness of each pattern. Once the search is complete, the ANNs are reused in sensitivity studies to give additional information on the performance of individual or clusters of wells.

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11 p.

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OSTI as DE95017296

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  • Advances in reservoir technology, London (United Kingdom), 7-8 Nov 1994

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  • Other: DE95017296
  • Report No.: UCRL-JC--118942
  • Report No.: CONF-9411254--1
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 103527
  • Archival Resource Key: ark:/67531/metadc624053

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Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • September 1, 1994

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  • June 16, 2015, 7:43 a.m.

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  • Feb. 18, 2016, 11:06 a.m.

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Johnson, V.M. & Rogers, L.L. Use of genetic algorithms and neural networks to optimize well locations and reduce well requirements, article, September 1, 1994; California. (digital.library.unt.edu/ark:/67531/metadc624053/: accessed November 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.