Integrated Data-Driven Framework for Forecasting Tight Gas Production Based on Machine Learning Algorithms, Feature Selection and Fracturing Optimization

A precise assessment of tight gas operational efficiency is critical for investment decisions in unconventional reservoir development. However, quantifying production efficiency remains challenging due to the complex relationships between geological and operational factors. This study proposes a novel data-driven framework for predicting tight gas productivity, effectively integrating computing algorithms, machine learning algorithms, feature selection, production prediction and fracturing parameter optimization. A dataset of 3146 horizontal wells from the Montney tight gas field was used to train six machine learning models, aiming to identify the most significant factors. Results indicate that fluid-injection volumes, burial depth, number of stages, Young's modulus, formation pressure, saturation, sandstone thickness and total organic carbon are the key variables for tight gas production. The Random Forest-based model achieved the highest accuracy of 88.6%. Case studies for the test demonstrate well that gas production could be nearly doubled by increasing fracturing fluid injection by 97.5%. This work provides evidence-based recommendations to refine development strategies and maximize reservoir performance.