(371ak) Enhancing Total Fracture Surface Area in Naturally Fractured Unconventional Reservoirs Via Model Predictive Control
The main objective of this study is to develop a model-based pumping schedule by utilizing a recently developed unconventional complex fracture propagation model called Mangrove describing complex fracture networks by accounting for interaction between the hydraulic fractures and natural fractures [4, 5]. One of the challenges is that Mangrove does not report the values of stimulated reservoir volume (SRV), and it is important to predict the SRV as it is the only available real-time measurement during the hydraulic fracturing process using microseismic monitoring technique. Therefore, we developed a new subroutine in Mangrove to calculate the SRV. Then, we constructed a surrogate model that describes the relationship between the manipulated input variables (i.e., fracturing fluid injection rate and proppant concentration at the wellbore) and output variables (SRV and total fracture surface area) using the data generated from the Mangrove with the proposed subroutine. Next, we designed a Kalman filter utilizing the measurement of SRV to estimate the total fracture surface area. Then, a model-based feedback control system is proposed to determine the fracturing fluid pumping schedule that maximizes the total fracture surface area. The closed-loop simulation results demonstrate that the obtained total fracture surface area can lead to enhanced oil and gas production rates from naturally fractured unconventional reservoirs.
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