Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network

Abstract

Oil production forecasting is an important means of understanding and effectively developing reservoirs. Reservoir numerical simulation is the most mature and effective method for production forecasting, but its accuracy mostly depends on high-quality history matching and accurate geological models. In order to achieve fast and accurate production predicting, an ensemble empirical mode decomposition (EEMD) based Long Short-Term Memory (LSTM) learning paradigm is proposed for oil production forecasting. In this paper, the original oil production series are first split into training set and test set. The data of test set is gradually added to the training set and decomposed by EEMD to obtain multiple intrinsic mode functions (IMFs). The stability of IMFs is analyzed by its Means and curve similarity computed by Dynamic time warping (DTW). Then proper number of stable IMFs are selected as predictor variables for machine learning. Considering the variation trend and context information of production series, LSTM is utilized to establish predictive model for production forecasting. The optimal hyper-parameters of LSTM are determined by Genetic algorithm (GA). For evaluation and verification purpose, the proposed model is applied to two actual oilfields from China. Empirical results demonstrated that the proposed approach is capable of giving almost perfect production forecasting.