Approximate Dynamic Programming Based Control of Proppant Concentration in Hydraulic Fracturing

Harwinder Singh Sidhu, Prashanth Siddhamshetty and Joseph S. Kwon
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Harwinder Singh Sidhu: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
Prashanth Siddhamshetty: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
Joseph S. Kwon: Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA

Mathematics, 2018, vol. 6, issue 8, 1-19

Abstract: Hydraulic fracturing has played a crucial role in enhancing the extraction of oil and gas from deep underground sources. The two main objectives of hydraulic fracturing are to produce fractures with a desired fracture geometry and to achieve the target proppant concentration inside the fracture. Recently, some efforts have been made to accomplish these objectives by the model predictive control (MPC) theory based on the assumption that the rock mechanical properties such as the Young’s modulus are known and spatially homogenous. However, this approach may not be optimal if there is an uncertainty in the rock mechanical properties. Furthermore, the computational requirements associated with the MPC approach to calculate the control moves at each sampling time can be significantly high when the underlying process dynamics is described by a nonlinear large-scale system. To address these issues, the current work proposes an approximate dynamic programming (ADP) based approach for the closed-loop control of hydraulic fracturing to achieve the target proppant concentration at the end of pumping. ADP is a model-based control technique which combines a high-fidelity simulation and function approximator to alleviate the “curse-of-dimensionality” associated with the traditional dynamic programming (DP) approach. A series of simulations results is provided to demonstrate the performance of the ADP-based controller in achieving the target proppant concentration at the end of pumping at a fraction of the computational cost required by MPC while handling the uncertainty in the Young’s modulus of the rock formation.

Keywords: approximate dynamic programming (ADP); model predictive control (MPC); hydraulic fracturing; model reduction; Kalman filter (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2018
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