Well-Test Model Identification With Self-Organizing Feature Map

Abstract

Summary Well-test data have been used traditionally for determining a variety of reservoir parameters, such as average permeability, storage capacity, reservoir damage, presence of faults and fractures, and reservoir mechanism. A number of techniques, both conventional methods, such as type-curve matching and numerical simulation, and artificial intelligence (AI) methods, have been used for identifying well-test models. These methods are laborious and time-consuming and at times give incorrect results. Artificial neural networks (ANN's) are recent developments in computer vision and image analysis. These networks are specialized computer software that generate a strategy to produce nonlinear mapping functions for complex problems. ANN's are commonly used as a tool for recognizing an object or predicting an event given an associated pattern. Only a limited number of applications of ANN for analyzing well-test data have been reported. These applications are mostly model-specific (developed for specific reservoir models) and, hence, are not general enough. This paper presents a new method based on ANN's that uses Ko-honen's self-organizing feature (SOF) mapping technique to identify well-test interpretation models. By grouping well-test data into distinct categories, the SOF algorithm produces a general mapping function. This method can help analyze well-test data from a large variety of reservoirs (including reservoirs with faults, fractures, boundaries, etc.) more efficiently and inexpensively than was feasible previously.