Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation. (July 2020)

• Record Type: Journal Article
• Authors: Narasingam, Abhinav; Kwon, Joseph Sang-Il
• Is Part Of: Journal of process control. Volume 91(2020)
• Page Start: 25
• Page End: 36
• Publication Date: 2020-07
• Subjects: Koopman operator -- Model predictive control -- Distributed parameter systems -- Moving boundary problem -- Hydraulic fracturing -- Extended dynamic mode decomposition; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.05.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled dynamics exhibited by the process. Koopman operator theory is particularly attractive here as it offers a way to explicitly construct linear representations for even highly nonlinear dynamics. The method is data-driven and relies on lifting the states to an infinite-dimensional space of functions called observables where the dynamics are governed by a linear Koopman operator. This work considers two problems: (a) fracture geometry control, and (b) proppant concentration control. In both cases, an approximate linear model of the corresponding dynamics is constructed and used to design a model predictive controller (MPC). The manuscript shows that in the case of highly nonlinear dynamics, as observed in the proppant concentration, use of canonical functions in the observable basis fails. In such cases, a priori system knowledge can be leveraged to choose the required basis. The numerical experiments demonstrate that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired target values maximizing the oil and gas productivity. Additionally, due to its linear structure, the Koopman models allow convex MPC formulations that avoid any issues associated with nonlinear optimization. Highlights: Koopman operator-based MPC is developed for a hydraulic fracturing process. Koopman system identification is used to approximate nonlinear dynamics. Linear Koopman models when used in MPC lead to convex quadratic formulations. A priori system knowledge can be incorporated to curate the Koopman basis. Superior closed-loop performance in regulating fracture geometry and concentration.