摘要：

CO₂驱油技术作为油藏高效开发与碳封存协同推进的关键手段，其应用过程中面临的CO₂气窜问题，已成为限制采收率提升与碳封存安全性的重要工程难题。因此，准确预测CO₂气窜时机并采取有效防控措施，对保障 CO₂驱开发效益、提升碳封存稳定性具有不可替代的重要意义。然而，现有预测方法在实际应用中面临诸多挑战：(1)传统定量表征模型依赖特定油藏参数，难以适应不同类型油藏的预测需求；(2)气窜发生前期气油比接近于零，数据呈现高维、小样本、稀疏等特征，导致传统经验公式方法在捕捉复杂非线性模式方面表现有限；(3) 储层中高渗带和裂缝等优势渗流通道分布具有随机性，其渗透率远高于基质渗透率，且通道参数难以精准表征，进一步加剧了CO₂气窜发生时机预测的复杂性和不确定性。为解决这些问题，本研究提出了一种融合多源域深度迁移学习与Stacking集成学习的智能预测方法。该方法采用多源异构数据训练策略，通过自适应技术实现不同油藏间的知识迁移，并采用了支持向量回归(Support Vector Regression, SVR)、决策树(Decision Tree, DT)、随机森林(Random Forest, RF)、K近邻(K-Nearest Neighbors Algorithm, KNN)4种基模型构成Stacking集成框架，融合各基学习器的预测优势，有效提升对CO₂气窜时机这类高维小样本数据的拟合能力与复杂模式捕捉精度。矿场应用结果表明，该方法具有优异的预测性能，决定系数(Coefficient of Determination, R²)达0.96，均方误差(Mean Squared Error, MSE)为12.61，平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)低至6.04%。本研究不仅为CO₂气窜预测提供了更加精准与智能化的技术路径，还展现了良好的通用性和跨油藏适应能力，为不同类型油藏的CO₂驱开发优化提供了新的解决方案，对提升原油采收率、降低开发风险以及实现大规模碳封存目标具有重要的工程应用价值。同时也为天然气窜流、水窜等其他流体窜流时机的预测与防控研究提供了有益的借鉴与参考。

关键词: CO₂气窜, 深度迁移学习策略, Stacking模型, CO₂提高采收率

Abstract:

CO₂ flooding is a key technology for achieving the dual objectives of efficient reservoir development and carbon sequestration. However, the occurrence of CO₂ channeling during the flooding process has emerged as a critical engineering challenge, severely restricting improvements in oil recovery and compromising the safety and efficiency of carbon storage. The Accurate prediction of CO₂ channeling onset, coupled with the timely deployment of effective mitigation strategies, is indispensable for safeguarding the economic benefits of CO₂ flooding, ensuring the long-term integrity of carbon storage, maximizing incremental oil recovery, and minimizing the operational and environmental risks associated with premature gas channeling. Despite its importance, existing prediction methods face several practical challenges: (1) conventional quantitative characterization models rely heavily on specific reservoir parameters, limiting their applicability to diverse reservoir conditions; (2) in the early stages prior to channeling onset, the gas-oil ratio is close to zero, with the available data typically high-dimensional, small-sample, and sparse-features that hinder empirical formula-based methods from effectively capturing complex nonlinear patterns; (3) high-permeability zones and fractures, which form preferential flow channels, are randomly distributed within the reservoir, exhibit permeability far exceeding that of the matrix, and are difficult to characterize accurately, thereby exacerbating the complexity and uncertainty of CO₂ channeling prediction. To address these challenges, this work proposes an intelligent prediction framework that integrates multi-source domain deep transfer learning with a Stacking ensemble learning strategy. A multi-source heterogeneous data training approach is employed to enable adaptive knowledge transfer across reservoirs. Four base learners-Support Vector Regression, Decision Tree, Random Forest, and K-Nearest Neighbors—are incorporated into the Stacking ensemble model, leveraging their complementary prediction strengths to improve fitting performance and enhance the accuracy of complex pattern recognition in the high-dimensional, small-sample datasets associated with CO₂ channeling onset prediction. Field application results demonstrate the method’s outstanding predictive performance, achieving a coefficient of determination (R²) of 0.96, mean squared error (MSE) of 12.61, and mean absolute percentage error (MAPE) as low as 6.04%. This work not only provides a more precise and intelligent technical pathway for CO₂ channeling prediction but also exhibits strong generalizability and cross-reservoir adaptability. It offers a new solution for optimizing CO₂ flooding across different reservoir types, with significant engineering value for enhancing oil recovery, reducing development risks, and achieving large-scale carbon sequestration goals. Furthermore, the proposed approach offers valuable methodological insights and transferable principles for the prediction and mitigation of other fluid channeling timing, including gas channeling and water channeling.

Key words: CO₂ gas channeling, deep transfer learning strategy, Stacking model, CO₂ enhanced oil recovery

中图分类号: 

• TE341

• TP181