Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. [Quarterly report], January 1--March 31, 1995

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Description

This basis search is to apply novel techniques from Artificial Intelligence (AI) and Expert Systems in capturing, integrating and articulating key knowledge from geology, geostatistics, and petroleum engineering to develop accurate descriptions of petroleum reservoirs. The ultimate goal is to design and implement a single powerful expert system for use by small producers and independents to efficiently exploit reservoirs. The overall project plan to design the system to create integrated reservoir description begins by initially developing an AI-based methodology for producing large-scale reservoir descriptions generated interactively from geology and well test data. Parallel to this task is a second task ... continued below

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21 p.

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Kelkar, B.G.; Kerr, D.R.; Thompson, L.G. & Shenoi, S. July 1, 1995.

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Description

This basis search is to apply novel techniques from Artificial Intelligence (AI) and Expert Systems in capturing, integrating and articulating key knowledge from geology, geostatistics, and petroleum engineering to develop accurate descriptions of petroleum reservoirs. The ultimate goal is to design and implement a single powerful expert system for use by small producers and independents to efficiently exploit reservoirs. The overall project plan to design the system to create integrated reservoir description begins by initially developing an AI-based methodology for producing large-scale reservoir descriptions generated interactively from geology and well test data. Parallel to this task is a second task that develops an AI-based methodology that uses facies-biased information to generate small-scale descriptions of reservoir properties such as permeability and porosity. The third task involves consolidation and integration of the large-scale and small-scale methodologies to produce reservoir descriptions honoring all the available data. The final task will be technology transfer. With this plan, we have carefully allocated and sequenced the activities involved in each of the tasks to promote concurrent progress towards the research objectives. The results of the integration are not merely limited to obtaining better characterizations of individual reservoirs. They have the potential to significantly impact and advance the discipline of reservoir characterization itself.

Physical Description

21 p.

Notes

OSTI as DE95014902

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  • Other Information: PBD: [1995]

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  • Other: DE95014902
  • Report No.: DOE/BC/14894--7
  • Grant Number: AC22-93BC14894
  • DOI: 10.2172/94027 | External Link
  • Office of Scientific & Technical Information Report Number: 94027
  • Archival Resource Key: ark:/67531/metadc791622

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Creation Date

  • July 1, 1995

Added to The UNT Digital Library

  • Dec. 19, 2015, 7:14 p.m.

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  • Jan. 4, 2016, 11:34 a.m.

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Kelkar, B.G.; Kerr, D.R.; Thompson, L.G. & Shenoi, S. Application of artificial intelligence to reservoir characterization: An interdisciplinary approach. [Quarterly report], January 1--March 31, 1995, report, July 1, 1995; United States. (digital.library.unt.edu/ark:/67531/metadc791622/: accessed September 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.