Use of Artificial Intelligence for Model Identification and Parameter Estimation in Well Test Interpretation
Abstract/Contents
- Abstract
- This thesis describes the development of techniques for the automation of the model identification step of a well test interpretation, using Artificial Intelligence. The computer's choice of a model is based on the pressure derivative curve, and simulates the visual diagnosis performed by a human expert. Most of the reasoning involved in such a diagnosis uses a symbolic representation of the derivative curve, which in the case of a human expert is built almost unconsciously. Techniques were developed to replicate this perception step. A major difficulty in the analysis of real data, particularly using the pressure derivative, is the separation of the true reservoir response from signal or differentiation noise. Again, this is relatively simple for a human observer, but difficult to implement in a computer program. This thesis describes an algorithm developed to overcome this problem. The algorithm was able to distinguish response from noise correctly, therefore permitting a correct model identification. In a manner analogous to a human expert, the technique constructs an interpretation model by combining the features of the different flow periods of the pressure response, for the entire duration of a test. The adequacy of a model is determined by qualitative as well as quantitative information. Once a model is chosen, its parameters are estimated using correlations, or an appropriate table. The system developed in this work can perform model identification on analytical as well as real data. Since the methodology also provides parameter estimation, it can be used together with an automated type curve matching analysis.
Description
| Type of resource | text |
|---|---|
| Date created | December 1988 |
Creators/Contributors
| Author | Allain, Olivier |
|---|---|
| Primary advisor | Horne, Roland N. |
| Advisor | Ramey, Jr., Henry J. |
| Degree granting institution | Stanford University, Department of Petroleum Engineering |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Genre | Thesis |
Bibliographic information
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Preferred citation
- Preferred Citation
- Allain, Olivier. (1988). Use of Artificial Intelligence for Model Identification and Parameter Estimation in Well Test Interpretation. Stanford Digital Repository. Available at: https://purl.stanford.edu/cc465tw2838
Collection
Master's Theses, Doerr School of Sustainability
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