An Artificial Neural Network Approach to Identify the Well Test Interpretation Model: Applications

Abstract

Abstract The objective of this paper is to present the application of a new approach to identify a preliminary well test interpretation model from derivative plot data. Our approach is based on artificial neural networks technology. In this approach, a neural nets simulator which employs backpropagation as the learning algorithm is trained on representative examples of derivative plots for a wide range of well test interpretation models. The trained nets are then used to identify the well test interpretation model from new well tests. In this paper we show that using artificial neural networks technology is a significant improvement over pattern recognition techniques currently used (e.g., syntactic pattern recognition) in well test interpretation. Artificial neural networks have the ability to generalize their understanding of the pattern recognition space they are taught to identify. This implies that they can identify patterns from incomplete and distorted data. This ability is very useful when dealing with well tests which often have incomplete and noisy data. Moreover, artificial neural networks eliminate the need for elaborate data preparation (e.g., smoothing, segmenting, and symbolic transformation) and they do not require writing complex rules to identify a pattern. Artificial neural networks eliminate the need for using rules by automatically building an internal understanding of the pattern recognition space in the form of weights that describe the strength of the connections between the net processing units. The paper illustrates the application of this new approach with a field example. The mathematical derivation and implementation of this approach can be found in Ref. 1.