(612h) Closed-Loop Integration of Scheduling and Offset-Free Model Predictive Control of Hydraulic Fracturing

The global increase in energy demand has accelerated the development of natural gas worldwide. The largest contributor to the long-term natural gas production growth in the United States is shale gas production, extracted through a combined use of horizontal drilling and hydraulic fracturing techniques [1]. Given the significant role that shale gas plays in the energy sector, a considerable number of optimization research has been done for the optimal design and operations of shale gas systems [2-6]. However, few of them consider the hydraulic fracturing operation as a dynamic process in the developed planning, and scheduling problems. Recently, several efforts have been made to apply model predictive control-based algorithms to hydraulic fracturing, where the pumping profile is computed online to achieve the desired proppant distribution and fracture geometry for enhanced well productivity [7, 8]. Since this pumping profile for hydraulic fracturing operation directly determines the amount of freshwater consumption and indirectly affects the amount of shale gas production, it is imperative to integrate the control of hydraulic fracturing with the scheduling problem of shale gas systems from the economic point of view.

Motivated by these considerations, we developed an integrated model to simultaneously consider the scheduling and control of hydraulic fracturing operations for the development of a set of wellpads. Particularly, a reduced-order model is developed based on high-fidelity simulation data and integrated with the scheduling model to reduce the model complexity. The linking variables are the amounts of freshwater required and shale gas forecasted at the wellpads. Then, to cope with the plant-model mismatch of the reduced-order model, we proposed an online integrated framework with two feedback loops. Specifically, in the outer loop, the integrated model is solved to determine the scheduling decisions and controller references. The obtained references are transferred to the inner loop, where a Kalman filter is utilized for state estimation and an offset-free model predictive control system is designed to track the references with enhanced performance while compensating for the plant-model mismatch. After the online control system is solved, the actual operation information is provided as the feedback to the outer loop for re-solving the integrated problem. To illustrate the effectiveness of the proposed framework, a hypothetical case study based on Marcellus Shale Play is considered. It shows that with the offset-free MPC, the undesirable performance degradation induced by the plant-model mismatch can be removed and the online implementation of the proposed framework can be further facilitated.

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