Comparative Analysis Between Empirical Correlations and Time Series Models for the Prediction and Forecasting of Unconventional Bakken Wells Production

Abstract

Abstract Accurately forecasting oil and gas well production, especially in complex unconventional reservoirs, is vital. Leveraging advanced techniques like machine learning and deep learning is becoming more common due to ample historical data availability. While traditional methods work for conventional reservoirs, they struggle in unconventional scenarios. Modern machine and deep learning models excel in such challenges, offering insights while bypassing temporary disruptions or pressure issues. This study compares ten empirical production forecast models with state-of-the-art deep learning and time series models (ARIMA, LSTM, GRU) in the Bakken shale play of the Williston Basin. After thorough calibration using extensive data, model efficacy is assessed using R2-score and MSE. Results highlight well-specific performance, with no single model consistently outperforming across all wells. Notably, optimally adjusted ARIMA produced commendable results for many wells. This research aids reservoir engineers by simplifying production decline trend identification, reducing reliance on intricate decline curve analyses. It ushers in a streamlined and dependable paradigm for production forecasting.