Abstract:

The strong heterogeneity of volcanic gas reservoirs and the multiple factors affecting the open flow potential of gas wells make it difficult for traditional methods of predicting the open flow potential of gas wells to balance computational efficiency and accuracy. In response to the above issues, this study introduces the data-driven Extreme Gradient Boosting algorithm (XGBoost) and proposes an algorithm that integrates the gas permeation mechanism and the data-driven approach to construct a single-well open-flow potential model for volcanic gas reservoirs based on the physically guided XGBoost algorithm (PG-XGBoost). This study is based on actual data from 50 gas wells in the Dixi block of the Kelameili gas field. Through the dual screening of the Mean Decrease Impurity (MDI) algorithm and Spearman correlation coefficient analysis, a comprehensive quantitative analysis is conducted on seven factors affecting the open flow potential of gas wells, including reservoir lithology, permeability, porosity, formation pressure, reservoir thickness, degree of fracture development, and fracturing treatment. The key factor for the open flow potential of gas wells is selected. Based on this, the XGBoost algorithm is used to construct a prediction model for the open flow potential of gas wells, and the binomial gas well productivity equation is used as the characterization formula for the gas seepage mechanism. Combining with the loss function of the XGBoost algorithm, a physics-guided XGBoost algorithm is constructed. Furthermore, the actual data of gas wells in the Dixi block are applied for blind well testing to evaluate the accuracy of the PG-XGBoost algorithm in predicting the open flow rate of gas wells. The results indicate that permeability, formation pressure, fracturing, reservoir lithology, and the degree of fracture development are the key factors for the open flow rate of a single well in the volcanic gas reservoir of the Dixi block in the Kelameili gas field. The PG-XGBoost algorithm was tested on 5 gas wells in this block, and the prediction accuracy of the open flow rate is 88.0%, which is 15.2% higher than that of the data-driven XGBoost algorithm. Therefore, using the binomial productivity equation as a physical constraint for the data-driven algorithm can effectively characterize non-Darcy gas flow and improve the prediction accuracy of the XGBoost algorithm for the gas well open flow rate. The method in this study can accurately predict the open flow potential of a single well in volcanic gas reservoirs, providing a technical path for predicting the open flow potential of complex gas reservoirs such as volcanic gas reservoirs.

Key words: gas well, open-flow potential, volcanic gas reservoir, physical-guided, XGBoost

摘要：

火山岩气藏非均质性强、气井无阻流量影响因素多，导致传统的气井无阻流量预测方法难以兼顾计算效率与精度。针对上述问题，本文引入数据驱动的极端梯度提升算法(XGBoost)，并提出融合气体渗流机理与数据驱动算法，构建基于物理引导XGBoost算法(PG-XGBoost)的火山岩气藏单井无阻流量模型。本文基于克拉美丽气田滴西区块50口气井的实际数据，通过平均不纯度减少算法(MDI)与斯皮尔曼相关系数的双重筛选，综合量化分析储层岩性、渗透率、孔隙度、地层压力、储层厚度、裂缝发育程度、压裂措施等7项气井无阻流量的影响因素，并选取气井无阻流量的主控因素。在此基础上，运用XGBoost算法构建气井无阻流量预测模型，并以二项式气井产能方程作为气体渗流机理的表征公式，与XGBoost算法的损失函数相结合，构建具有物理引导的XGBoost算法(PG-XGBoost)。进而应用滴西区块气井的实际数据进行盲井检验，评价PG-XGBoost算法的气井无阻流量预测精度。结果表明：渗透率、地层压力、压裂措施、储层岩性、裂缝发育程度是克拉美丽气田滴西区块火山岩气藏单井无阻流量的主控因素。经该区块5口气井的盲井检验，PG-XGBoost算法预测无阻流量的精度为88.0%，较完全由数据驱动的XGBoost算法，预测精度提升了15.2%。因此，以二项式产能方程等气体渗流机理作为数据驱动算法的物理约束，可有效表征气体非达西渗流，并提升XGBoost算法对气井无阻流量的预测精度。本文方法可实现火山岩气藏单井无阻流量的准确预测，为火山岩气藏等复杂气藏的无阻流量预测提供了可借鉴的技术路径。

关键词: 气井, 无阻流量, 火山岩气藏, 物理引导, XGBoost

CLC Number: 

• TE319

• TE371