Permeability estimation using a neural network: A case study from the Roberts unit, Wasson field, Yoakum County, Texas

Conference · Wed Apr 01 04:00:00 EST 1992 · AAPG Bulletin (American Association of Petroleum Geologists); (United States)

OSTI ID:6969384

Osborne, D A ^[1]

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1. Texaco Exploration and Production Inc., Midland, TX (United States)

Accurately estimating reservoir permeability is vital to reservoir simulation. The best method for determining reservoir permeability is to model core-derived permeability data. However, most Permian basin oil fields lack sufficient core coverage for core-based models. Therefore, the common method has been to develop linear relationships between core-derived porosity and permeability, then apply these relationships to porosity logs from noncored wells. This method has limitations because the linear relationships commonly are poor. Neural network technology provides an alternative method for determining reservoir permeability. Neural networks estimate permeability based on the relationships between many reservoir characteristics not just between porosity and permeability. Data from five cored wells in the San Andres (Upper Permian) reservoir of the Roberts unit were loaded into a neural network designed to predict permeabilities. This neural network had one hidden layer with 30 processing elements and used a sigmoid activation function. The network was trained in 3.1 million iterations using the geographic location of the cored well, the depth, the core porosity, and the specific reservoir flow unit as inputs, and the difference between the core-derived permeability and the linear-regression-derived permeability as the output. A 0.81 correlation coefficient was calculated for the neural-network-derived permeability values. This compares to a 0.44 correlation coefficient for the linear-regression-derived permeability values. Neural-network-derived permeabilities in noncored wells are consistent with production data, whereas linear regression-derived permeabilities are inconsistent.

• OSTI ID: 6969384
• Report Number(s): CONF-9204139--
• Conference Information: Journal Name: AAPG Bulletin (American Association of Petroleum Geologists); (United States) Journal Volume: 76:4
• Country of Publication: United States
• Language: English

Related Subjects

02 PETROLEUM
020200* -- Petroleum-- Reserves
Geology
& Exploration
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ACCURACY
CALCULATION METHODS
COMPUTER CALCULATIONS
DEVELOPED COUNTRIES
EXPERT SYSTEMS
FEDERAL REGION VI
FLOW MODELS
GEOLOGIC AGES
GEOLOGIC DEPOSITS
GEOLOGIC FORMATIONS
MATHEMATICAL MODELS
MINERAL RESOURCES
NEURAL NETWORKS
NORTH AMERICA
PALEOZOIC ERA
PERMEABILITY
PERMIAN BASIN
PERMIAN PERIOD
PETROLEUM DEPOSITS
POROSITY
PROBABILISTIC ESTIMATION
RESERVOIR ROCK
RESOURCES
TEXAS
USA