Forecasting Oil Production Flowrate Based on an Improved Backpropagation High-Order Neural Network with Empirical Mode Decomposition

Abstract

Developing a forecasting model for oilfield well production plays a significant role in managing mature oilfields as it can help to identify production loss earlier. It is very common that mature fields need more frequent production measurements to detect declining production. This study proposes a machine learning system based on a hybrid empirical mode decomposition backpropagation higher-order neural network (EMD-BP-HONN) for oilfields with less frequent measurement. With the individual well characteristic of stationary and non-stationary data, it creates a unique challenge. By utilizing historical well production measurement as a time series feature and then decomposing it using empirical mode decomposition, it generates a simpler pattern to be learned by the model. In this paper, various algorithms were deployed as a benchmark, and the proposed method was eventually completed to forecast well production. With proper feature engineering, it shows that the proposed method can be a potentially effective method to improve forecasting obtained by the traditional method.