Application of Artificial Intelligence to Reservoir Characterization - An Interdisciplinary Approach

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Description

The primary goal of this project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlation's between wells. Using the correlation's and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical ... continued below

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Medium: P; Size: 204 pages

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Kelkar, B.G.; Gamble, R.F.; Kerr, D.R.; Thompson, L.G. & Shenoi, S. January 12, 2000.

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Description

The primary goal of this project is to develop a user-friendly computer program to integrate geological and engineering information using Artificial Intelligence (AI) methodology. The project is restricted to fluvially dominated deltaic environments. The static information used in constructing the reservoir description includes well core and log data. Using the well core and the log data, the program identifies the marker beds, and the type of sand facies, and in turn, develops correlation's between wells. Using the correlation's and sand facies, the program is able to generate multiple realizations of sand facies and petrophysical properties at interwell locations using geostatistical techniques. The generated petrophysical properties are used as input in the next step where the production data are honored. By adjusting the petrophysical properties, the match between the simulated and the observed production rates is obtained.

Physical Description

Medium: P; Size: 204 pages

Notes

OSTI as DE00750056

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  • Other Information: PBD: 12 Jan 2000

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  • Report No.: DOE/BC/14894-16
  • Grant Number: AC22-93BC14894
  • DOI: 10.2172/750056 | External Link
  • Office of Scientific & Technical Information Report Number: 750056
  • Archival Resource Key: ark:/67531/metadc709994

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Creation Date

  • January 12, 2000

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

Description Last Updated

  • April 8, 2016, 2:24 p.m.

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Kelkar, B.G.; Gamble, R.F.; Kerr, D.R.; Thompson, L.G. & Shenoi, S. Application of Artificial Intelligence to Reservoir Characterization - An Interdisciplinary Approach, report, January 12, 2000; Tulsa, Oklahoma. (digital.library.unt.edu/ark:/67531/metadc709994/: accessed August 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.