A framework for predicting the production performance of unconventional resources using deep learning. (1st August 2021)

• Record Type: Journal Article
• Authors: Wang, Sen; Qin, Chaoxu; Feng, Qihong; Javadpour, Farzam; Rui, Zhenhua
• Is Part Of: Applied energy. Volume 295(2021)
• Publication Date: 2021-08-01
• Subjects: Deep learning -- Unconventional resources -- Numerical simulation -- Deep belief network -- Prediction -- Hyperparameter optimization; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.117016
• Languages: English
• ISSNs: 0306-2619

Abstract

Graphical abstract: Highlights: Deep belief network models are developed to forecast reservoir production dynamics. We construct a general prediction framework for unconventional resources. Optimizing hyperparameters using the Bayesian algorithm improves model performance. Theframework is applied to the Bakken shale and provides excellent performance. The study shed lights on the construction of data-driven models in the energy area. Abstract: 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.