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:
- 6859758
- Report Number(s):
- CONF-960527--
- Journal Information:
- AAPG Bulletin, Journal Name: AAPG Bulletin Vol. 5; ISSN 0149-1423; ISSN AABUD2
- Country of Publication:
- United States
- Language:
- English
Similar Records
Neural networks: Prediction of carbonate lithology and permeability from wireline logs in a miocene buildup, offshore Sarawak
Reservoir characterization of the giant Hugoton gas field, Kansas