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Abstract

Summary

Closed-loop reservoir management (CLRM) consists of near-continuous data assimilation and real-time optimization to improve oil recovery and reservoir economics. In deep oil sands deposits using steam-assisted gravity drainage (SAGD) recovery process, CLRM involves real-time subcool (difference between actual and saturation temperature) control to develop the uniform steam chamber along the horizontal injector-producer well pair. Recently, model predictive control (MPC) has been implemented to maintain the optimal subcool; however, oversimplified models used in MPC are inadequate as reservoir dynamics in SAGD is highly complex, spatially distributed, and nonlinear. This provides an opportunity for the improved CLRM workflow which can incorporate the nonlinear physical/empirical models in MPC to represent the flow dynamics accurately over the reservoir lifecycle.

In this research, two novel workflows, comprising linearization and nonlinear optimization are proposed to implement nonlinear model predictive control (NMPC) in CLRM of SAGD reservoirs. Linearization basically reduces an NMPC problem to linear MPC by estimating an equivalent linear model of a nonlinear black box model for a given input signal in a mean-square-error sense. Due to linear approximation, cost function in the MPC can be minimized using quadratic programming (QP) over the specified time horizon. Another approach is to use nonlinear dynamic models directly for accurate prediction of the plant states and/or outputs. Resulting nonconvex, nonlinear cost optimization problem is solved using interior-point algorithm at each control interval. Proposed workflows are tested using the history-matched, field-scale model of a SAGD reservoir located in northern Alberta, Canada. The horizontal well pair with dual-tubing string completion is segmented and subcool in each section is considered as an output variable while steam injection rates in both tubings and liquid production rate are the input variables of the NMPC controller. Bi-directional communication link was established between the controller and thermal reservoir simulator, acting as a virtual process plant. Qualitative and quantitative analysis of the results reveals that nonlinear black-box models can successfully capture the nonlinearity of the SAGD process in CLRM. Also, both workflows can control the subcool above desired set-point while ensuring the stable well operations. Furthermore, net-present-value (NPV) is increased by 24% when proposed NMPC workflows are used in CLRM as compared to the base case with no closed-loop control. Overall, NMPC can be successfully employed in CLRM of SAGD reservoirs for improved real-time subcool control, energy efficiency, and greenhouse gas emissions while satisfying the constraints offered by the surface facilities.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.201802217
2018-09-03
2024-04-25

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Closed-Loop Reservoir Management Using Nonlinear Model Predictive Control: A Field Case Study
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802217
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