A Study on a Novel Production Forecasting Method of Unconventional Oil and Gas Wells Based on Adaptive Fusion

Abstract

Reliable forecasting of unconventional oil and gas well production has consistently been a hot and challenging issue. Most existing data-driven production forecasting models rely solely on a single methodology, with the application effects of other mainstream algorithms remaining unclear, which to some extent hinders the generalization and utilization of these models. To address this, this study commences with data preparation and systematically develops a novel forecasting model based on the adaptive fusion of multiple mainstream data-driven algorithms such as random forest and support vector machine. The validity of the model is verified using actual production wells in the Marcellus. A comprehensive evaluation of multiple feature engineering extraction techniques concludes that the main controlling factors affecting the production of Marcellus gas wells are horizontal segment length, fracturing fluid volume, vertical depth, fracturing section, and reservoir thickness. Evaluation models based on these primary controlling factors reveal significant differences in prediction performance among mainstream data-driven methods when applied to the dataset. The newly developed model based on adaptive fusion optimized by genetic algorithms outperforms individual models across various evaluation metrics, which can effectively improve the accuracy of production forecasting, demonstrating its potential for promoting the application of data-driven methods in forecasting unconventional oil and gas well production. Furthermore, this will assist enterprises in allocating resources more effectively, optimizing extraction strategies, and reducing potential costs stemming from inaccurate predictions.