Li Weirong, Zhang Tianyang, Liu Xinju, Dong Zhenzhen, Dong Guoqing, Qian Shihao, Yang Zhanrong, Zou Lu, Lin Keze, Zhang Tao
Xi'an Shiyou University, Xi'an, 710065, China.
China University of Petroleum (Beijing), Beijing, 102249, China.
Sci Rep. 2024 Mar 13;14(1):6046. doi: 10.1038/s41598-024-56660-8.
In the process of developing tight oil and gas reservoirs, multistage fractured horizontal wells (NFHWs) can greatly increase the production rate, and the optimal design of its fracturing parameters is also an important means to further increase the production rate. Accurate production prediction is essential for the formulation of effective development strategies and development plans before and during project execution. In this study, a novel workflow incorporating machine learning (ML) and particle swarm optimization algorithms (PSO) is proposed to predict the production rate of multi-stage fractured horizontal wells in tight reservoirs and optimize the fracturing parameters. The researchers conducted 10,000 numerical simulation experiments to build a complete training and validation dataset, based on which five machine learning production prediction models were developed. As input variables for yield prediction, eight key factors affecting yield were selected. The results of the study show that among the five models, the random forest (RF) model best establishes the mapping relationship between feature variables and yield. After verifying the validity of the Random Forest-based yield prediction model, the researchers combined it with the particle swarm optimization algorithm to determine the optimal combination of fracturing parameters under the condition of maximizing the net present value. A hybrid model, called ML-PSO, is proposed to overcome the limitations of current production forecasting studies, which are difficult to maximize economic returns and optimize the fracturing scheme based on operator preferences (e.g., target NPV). The designed workflow can not only accurately and efficiently predict the production of multi-stage fractured horizontal wells in real-time, but also be used as a parameter selection tool to optimize the fracture design. This study promotes data-driven decision-making for oil and gas development, and its tight reservoir production forecasts provide the basis for accurate forecasting models for the oil and gas industry.
在致密油气藏开发过程中，多级压裂水平井可大幅提高产量，其压裂参数的优化设计也是进一步提高产量的重要手段。准确的产量预测对于在项目执行前和执行期间制定有效的开发策略和开发计划至关重要。在本研究中，提出了一种结合机器学习（ML）和粒子群优化算法（PSO）的新颖工作流程，以预测致密油藏中多级压裂水平井的产量并优化压裂参数。研究人员进行了10000次数值模拟实验，构建了一个完整的训练和验证数据集，并在此基础上开发了五个机器学习产量预测模型。作为产量预测的输入变量，选择了影响产量的八个关键因素。研究结果表明，在这五个模型中，随机森林（RF）模型最能建立特征变量与产量之间的映射关系。在验证了基于随机森林的产量预测模型的有效性后，研究人员将其与粒子群优化算法相结合，以确定在净现值最大化条件下压裂参数的最优组合。提出了一种名为ML - PSO的混合模型，以克服当前产量预测研究的局限性，即难以实现经济回报最大化并根据运营商偏好（例如目标净现值）优化压裂方案。所设计的工作流程不仅能准确、高效地实时预测多级压裂水平井的产量，还可作为参数选择工具来优化压裂设计。本研究推动了油气开发的数据驱动决策，其对致密油藏的产量预测为油气行业准确的预测模型提供了依据。
