Data-driven approach for hydrocarbon production forecasting using machine learning techniques

Abstract

Main aspect of this study is to explore the possible use of Machine learning based approaches in the petroleum industry. The fourth industrial revolution is being ushered in by advances in computer power. The oil and gas sector contains a huge amount of produced oil data from wells that can be utilised to create data-driven models which can validate empirical correlations. This validation is vital in the current context because the oil market fluctuates a lot, thus making efficient judgments based on insights supplied by data science and machine learning algorithms are valuable. Artificial Neural Network (ANN), Random Forest Regressor (RF) and Gradient Boosting Regressor (GB) were used in this study to forecast daily oil production using available production parameters. In this study, the production data from Equinor's Volve Field was utilised. The obtained data was filtered, and then it was applied to mentioned algorithms with fine tuned hyperparameters. The data was split into training data (70%) and test data (30%). The model was constructed on training data then it was validated through test data. Seven parameters were chosen on the basis of magnitude of correlation coefficient. The result showed that even with the cases of several shut-ins, the hydrocarbon forecasting method for well 159-F-1C predicted production rates with a coefficient of determination (R2score) value greater or equal to 0.90. For performance analysis, coefficient of determination (R2score), mean squared error (MSE) and mean absolute error (MAE) were used for the selected wells. The outcome showed that prediction of hydrocarbon production for well 15/9-F-1 C ANN model showed the best performance (R2 score = 0.9, MAE = 20.52 and MSE = 1268) and for well 15/9-F-12 RF model shows the best performance (R2 score = 0.98, MAE = 84.95 and MSE = 37356.07). This novel approach could be trained on any dataset to assist in oil forecasting.