Statistics

Filter (signal processing)

Operating system

Stability (learning theory)

Algorithm

Machine learning

Physics

Data mining

Macroeconomics

Paleontology

Artificial intelligence

Economics

Matching (statistics)

Test set

Set (abstract data type)

Programming language

Hilbert–Huang transform

Production (economics)

Context (archaeology)

Computer vision

Mathematics

Computer science

Mode (computer interface)

Artificial neural network

Term (time)

Quantum mechanics

Biology

Dynamic time warping

Journal of Petroleum Science and Engineering

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.

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