Abstract
Petroleum well test analysis is a tool for estimating the average properties of the reservoir rock. It is a classic example of an inverse problem. Visual examination of the pressure response of the reservoir to an induced flow rate change at a well allows the experienced analyst to determine the most appropriate model from a library of generalized analytical solutions. Rock properties are determined by finding the model parameters that best fit the observed data. This paper describes a framework for hybrid network to assist the analyst in selecting the appropriate model and determining the solution. The hybrid network design offers significant advantages by reducing training time and allowing incorporation of both symbolic and numeric data. The network structure is described and the advantages and disadvantages compared to previous approaches are discussed
| Original language | American English |
|---|---|
| Journal | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks |
| DOIs | |
| State | Published - Jan 1 1998 |
Keywords
- Generalized Analytical Solution Library
- Geology
- Geophysical Prospecting
- Geophysical Techniques
- Geophysics Computing
- Hybrid Neural Network
- Hybrid System
- Induced Flow Rate Change
- Inverse Problems
- Measurement Technique
- Neural Nets
- Numeric Data
- Oil Well
- Parameter Estimation
- Petroleum Industry
- Petroleum Well Test Analysis
- Pressure Response
- Reservoir Rock
- Rocks
- Symbolic Data
- Visual Examination
- Well Test Analysis
Disciplines
- Operations Research, Systems Engineering and Industrial Engineering
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