Use of artifical neural nets to predict permeability in Hugoton Field
- Amoco MidContinent Business Unit, Denver, CO (United States)
One of the most difficult tasks in petrophysics is establishing a quantitative relationship between core permeability and wireline logs. This is a tough problem in Hugoton Field, where a complicated mix of carbonates and clastics further obscure the correlation. One can successfully model complex relationships such as permeability-to-logs using artificial neural networks. Mind and Vision, Inc.`s neural net software was used because of its orientation toward depth-related data (such as logs) and its ability to run on a variety of log analysis platforms. This type of neural net program allows the expert geologist to select a few (10-100) points of control to train the {open_quotes}brainstate{close_quotes} using logs as predicters and core permeability as {open_quotes}truth{close_quotes}. In Hugoton Field, the brainstate provides an estimate of permeability at each depth in 474 logged wells. These neural net-derived permeabilities are being used in reservoir characterization models for fluid saturations. Other applications of this artificial neural network technique include deterministic relationships of logs to: core lithology, core porosity, pore type, and other wireline logs (e.g., predicting a sonic log from a density log).
- OSTI ID:
- 425905
- Report Number(s):
- CONF-960527-; TRN: 96:004994-0546
- Resource Relation:
- Conference: Annual convention of the American Association of Petroleum Geologists, Inc. and the Society for Sedimentary Geology: global exploration and geotechnology, San Diego, CA (United States), 19-22 May 1996; Other Information: PBD: 1996; Related Information: Is Part Of 1996 AAPG annual convention. Volume 5; PB: 231 p.
- Country of Publication:
- United States
- Language:
- English
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