摘要：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.

关键字：machine learning; gas production forecasting; Montney Formation; influencing parameters; fracturing optimization

ISSN号：2227-9717

卷、期、页：卷13期4

发表日期：2025-04-11

影响因子：0.000000

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：PROCESSES

参与作者：孟德伟,张柯,任义丽,武丹,顾斐

通讯作者：姚福裕,葛琛琦,李烨,张雨杰,葛琛琦,张宇杰,包鹏虎,皮之洋

第一作者：惠钢

论文类型：期刊论文

论文概要：姚福裕,惠钢,孟德伟,葛琛琦,张柯,任义丽,李烨,张雨杰,葛琛琦,张宇杰,包鹏虎,皮之洋,武丹,顾斐，Integrated Data-Driven Framework for Forecasting Tight Gas Production Based on Machine Learning Algorithms, Feature Selection and Fracturing Optimization，PROCESSES，2025，卷13期4

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