期刊
APPLIED ENERGY
卷 295, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.117016
关键词
Deep learning; Unconventional resources; Numerical simulation; Deep belief network; Prediction; Hyperparameter optimization
资金
- National Natural Science Foundation of China [51704312, U1762213]
- Major Scientific and Technological Projects of CNPC [ZD2019183007]
- Applied Fundamental Research Project of Qingdao [196221cg]
- Nanogeosciences Laboratory at the Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin
- Mudrock Systems Research Laboratory (MSRL) at the Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin
Developed deep belief network (DBN) models for predicting the production performance of unconventional wells effectively and accurately, showing higher prediction accuracy and generalization ability than traditional machine-learning techniques. Optimized fracturing design using the trained DBN model yielded outstanding results, demonstrating its potential as a powerful tool in optimizing fracturing designs.
Predicting the production performance of multistage fractured horizontal wells is essential for developing unconventional resources such as shale gas and oil. Accurate predictions of the production performance of wells that have not been put into production are necessary to optimize hydraulic fracture parameters prior to operation. However, traditional analytic methods are made inefficient by their strong dependency on historical production data and their huge computational expense. To conquer this issue, we developed deep belief network (DBN) models to predict the production performance of unconventional wells effectively and accurately. We ran 815 numerical simulation cases to construct a database for model training and optimized the hyperparameters of our network model using the Bayesian optimization algorithm. DBN models exhibit greater prediction accuracy and generalization ability than traditional machine-learning techniques such as back-propagation (BP) neural networks, and support vector regression (SVR). We also used the trained DBN model as a proxy to optimize the fracturing design and obtained outstanding results. Our proposed model could predict the production performance of an unconventional well instantaneously with considerable accuracy and shows excellent reusability, making it a powerful tool in optimizing fracturing designs. Our work lays a solid basis for anticipating the production performance of unconventional reservoirs and sheds light on the construction of data-driven models in the areas of energy conversion and utilization.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据