Time Series Analysis of Production Decline in Carbonate Reservoirs with Machine Learning

Abstract

Classical decline methods, such as Arps yield decline curve analysis, have advantages of simple principles and convenient applications, and they are widely used for yield decline analysis. However, for carbonate reservoirs with high initial production, rapid decline, and large production fluctuations, with most wells having no stable production period, the adaptability of traditional decline methods is inadequate. Hence, there is an urgent need to develop a new decline analysis method. Although machine learning methods based on multiple regression and deep learning have been applied to unconventional oil reservoirs in recent years, their application effects have been unsatisfactory. For example, prediction errors based on multiple regression machine learning methods are relatively large, and deep learning sample requirements and the actual conditions of reservoir management do not match. In this study, a new equal probability gene expression programming (EP-GEP) method was developed to overcome the shortcomings of the conventional Arps decline model in the production decline analysis of carbonate reservoirs. Through model validation and comparative analysis of prediction effects, it was proven that the EP-GEP model exhibited good prediction accuracy, and the average relative error was significantly smaller than those of the traditional Arps model and existing machine learning methods. The successful application of the proposed method in the production decline analysis of carbonate reservoirs is expected to provide a new decline analysis tool for field reservoir engineers.