Deep reinforcement learning control of hydraulic fracturing
- Texas A & M University, College Station, TX (United States); OSTI
- Texas A & M University, College Station, TX (United States)
Hydraulic fracturing is a technique to extract oil and gas from shale formations, and obtaining a uniform proppant concentration along the fracture is key to its productivity. Recently, various model predictive control schemes have been proposed to achieve this objective. But such controllers require an accurate and computationally efficient model which is difficult to obtain given the complexity of the process and uncertainties in the rock formation properties. In this article, we design a model-free data-based reinforcement learning controller which learns an optimal control policy through interactions with the process. Deep reinforcement learning (DRL) controller is based on the Deep Deterministic Policy Gradient algorithm that combines Deep-Q-network with actor-critic framework. In addition, we utilize dimensionality reduction and transfer learning to quicken the learning process. We show that the controller learns an optimal policy to obtain uniform proppant concentration despite the complex nature of the process while satisfying various input constraints.
- Research Organization:
- Texas A & M University, College Station, TX (United States)
- Sponsoring Organization:
- National Science Foundation (NSF); Texas A&M Energy Institute; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- Grant/Contract Number:
- EE0007888
- OSTI ID:
- 1977009
- Journal Information:
- Computers and Chemical Engineering, Journal Name: Computers and Chemical Engineering Journal Issue: C Vol. 154; ISSN 0098-1354
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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