Recognition and Classification for Inter-well Nonlinear Permeability Configuration in Low Permeability Reservoirs Utilizing Machine Learning Methods

Abstract

Machine learning methods have become the leading research algorithm enjoying popularity for reservoir engineering evaluation. In this paper, one machine learning method is selected and optimized for the recognition and classification of inter-well nonlinear permeability configurations between injection and production wells in the low permeability reservoir. The above configurations are divided into four classes, i.e., homogeneous, linear increment, convexity increasing (logarithmic function), and convex downward increasing (exponential function). According to four kinds of nonlinear permeability distributions in low permeability reservoirs and the increased effect of threshold pressure gradient, the productivity formula is established. Then the decision tree, neural networks (NN) and support vector machines (SVM) are utilized for training dynamic data under the influence of the training model, i.e., the configuration in low-permeability reservoirs. The data set is formed with dynamic production data under different configuration permeability, well spacing, thickness, pressure, and production. The recognition and classification of the permeability configuration are performed using different machine learning models. The results show that compared with NN and decision tree, SVM presents better performance in the accuracy of verification, true positive rate (TPR), false-negative rate (FNR) and receiver operating characteristic (ROC). Moreover, SVM verification results are placed on the brink of the training methods. This paper provides new insights and methods for the recognition and classification of inter-well nonlinear permeability configuration in low permeability reservoirs. Additionally, the research method can also apply to solve similar theoretical problems in other unconventional reservoirs.