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This paper discusses the parallel implementation of complex wells modeling in a large-scale simulation. Due to intensive computational requirements of giant reservoirs with hundreds of these complex wells, parallel implementation is essential for practical simulation studies. In this work, a distributed memory approach using message passing interface (MPI) is employed for parallelization where each processor is responsible for the computation of one or more complex wells. For inter-processor communication, a non-blocking technique is utilized to increase the parallel efficiency. Parallel implementation is not the only challenge for large-scale simulation. For instance, full-field simulation with hundreds of complex wells increases the probability of a nonconvergence solution for at least one or more complex wells during reservoir simulation. Robust algorithms are needed to guarantee convergence and improve performance. Therefore, in this paper, we propose treating complex wells as a subsurface network where it can be represented using graph theory. In addition, simulation results for a full-field with hundreds of intelligent complex wells are included. This will show the importance of well coupling with rigorous treatment of downhole controls and devices on accurately modeling large-scale and complex reservoir.
This paper discusses the parallel implementation of complex wells modeling in a large-scale simulation. Due to intensive computational requirements of giant reservoirs with hundreds of these complex wells, parallel implementation is essential for practical simulation studies. In this work, a distributed memory approach using message passing interface (MPI) is employed for parallelization where each processor is responsible for the computation of one or more complex wells. For inter-processor communication, a non-blocking technique is utilized to increase the parallel efficiency. Parallel implementation is not the only challenge for large-scale simulation. For instance, full-field simulation with hundreds of complex wells increases the probability of a nonconvergence solution for at least one or more complex wells during reservoir simulation. Robust algorithms are needed to guarantee convergence and improve performance. Therefore, in this paper, we propose treating complex wells as a subsurface network where it can be represented using graph theory. In addition, simulation results for a full-field with hundreds of intelligent complex wells are included. This will show the importance of well coupling with rigorous treatment of downhole controls and devices on accurately modeling large-scale and complex reservoir.
Traditional reservoir management relies on irregular information gathering operations such as surface sampling and production logging followed by one or several treatment operations. The availability of both diagnosis and the prescribed remedial operations can cause severe delays in the reservoir management cycle, increasing unplanned down-time and impacting cash flow. These effects can be exacerbated in remote and offshore fields where well intervention is time-intensive. A new, innovative, all-electric, flow control valve (FCV) is now a reality for smart completions. This can support any well penetration scenario including multiple zones per lateral in maximum reservoir contact wells and multi-trip completion in extended reach wells. Each zone is equipped with a permanent intelligent flow control valve, allowing real-time reservoir management and providing high-resolution reservoir control. Valve actuation is semi-instantaneous and field data has shown that the frequency of updating such valves is at least 50 times compared to conventional valves, enabling near continuous closed-loop reservoir management. However, such a high frequency optimization demands computational efficiency as it challenges existing optimization applications, particularly when multiple realizations are considered to account for reservoir uncertainty. In this paper, we present a framework to support field-wide implementation of smart FCVs and hence maintaining a fast closed-loop reservoir management. The framework consists of history matching using Ensemble Kalman Filters (EnKF) where smart FCV data is assimilated to condition a suite of representative reservoir models at a relatively high frequency. Thereafter, a reactive optimizer utilizing a non-linear programming method is applied with the objectives of maximizing instantaneous revenue by determining the optimal positions of the downhole valves under user defined rate, pressure drop, drawdown and setting constraints. This optimization offers production control planning suggestions with the intent of immediate to short-term gain in oil production based upon the downhole measurement and the performance of the near wellbore model. At the same time, a proactive optimizer can be used to determine valve-control settings for longer term objectives such as delaying water/gas breakthrough. The objective of this optimization is equalization of the water/gas front arrival times based upon generation of streamlines and time-of-flight (TOF) analysis. Both modes of optimization are performed efficiently such that a single optimization run is sufficient per geological realization. We use the OLYMPUS reference model, a water flooding case, to demonstrate the workflow. The reactive optimization shows an increase of 25% in the net present value through minimizing water production and increasing injection efficiency, while proactive optimization delays water breakthrough time by 2-4 years. The paper showcases the effectiveness of complementary workflows where high frequency reactive and proactive optimizations support a near continuous closed-loop reservoir management.
There is a growing interest in downhole flow control devices (FCD) as they can be used to counter the effects of reservoir heterogeneity and improve hydrocarbon recovery. The variety of FCD types, sizes, and specifications makes it challenging to select the right device, providing an optimal investment return. Reservoir engineers play an essential role in identifying and evaluating the possible options based on numerical simulation models. This paper utilizes a transient numerical optimizer, designed for FCD with active controls, as a generic tool for lower completion optimization, including passive and autonomous FCD. The workflow consists of five steps. The first step is to determine a practical number of completion zones in a well, given the reservoir heterogeneity and completion string considerations. The second step is to explore the potential gains of downhole control without accounting for device-specific limitations. Here, we integrate a local optimization method to run several simulations with reactive and proactive strategies. In the third step, we contrast the local optimization simulations results to determine the suitable family of FCD: passive, autonomous or active. In step four, we ensure the selected FCD family is correctly modelled in reservoir simulation, particularly for autonomous devices, based on a recently published method to calculate equipment specific flow coefficients. Finally, optimization runs are performed using the selected FCD family and specific coefficients. The workflow is demonstrated on a synthetic carbonate sector model with a ~2,000 m horizontal well. It can be shown that analyses of the local optimization results provide quick guidelines to screen and select suitable lower completion equipment for the well and reservoir. Furthermore, the local optimization results can be used to design the selected lower completion string. The suggested workflow is the first of its kind in that it caters to all FCD families. The workflow uses an efficient local optimization method in a next-generation reservoir simulator. The efficiency gains from using the optimizer allows the decision-making time required for FCD evaluation to be an order of magnitude less than the logging and completion operation timeframe. Hence, the workflow enables efficient real-time design, planning and optimization of FCD.
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