Distributed Agent Optimization for Large-Scale Network Models

Abstract

Abstract Optimization of production networks is key for managing efficient hydrocarbon production as part of closed-loop asset management. Large-scale surface network optimization is a challenging task that involves high nonlinearity with numerous constraints. In existing tools, the computational cost of solving the surface network optimization can exponentially increase with the size and complexities of the network using traditional approaches involving nonlinear programming methods. In this study, we accelerate the large-scale surface network optimization by using a distributed agent optimization algorithm called alternating direction method of multipliers (ADMM). We develop and apply the ADMM algorithm for large-scale network optimization with over 1000 wells and interconnecting pipelines. In the ADMM framework, a large-scale network system is broken down into many small sub-network systems. Then, a smaller optimization problem is formulated for each sub-network. These sub-network optimization problems are solved in parallel using multiple computer cores so that the entire system optimization will be accelerated. A large-scale surface network involves many inequality and equality constraints, which are effectively handled by using augmented Lagrangian method to enhance the robustness of convergence quality. Additionally, proxy or hybrid models can also be used for pipe flow and pressure calculation for every network segment to further speed up the optimization. The proposed ADMM optimization method is validated by several synthetic cases. We first apply the proposed method to surface network simulation problems of various sizes and complexities (configurations, fluid types, pressure regimes, etc.), where the pressure for all nodes and fluxes in all links will be calculated with a specified separator pressure and reservoir pressures. High accuracy was obtained from the ADMM framework compared with a commercial simulator. Next, the ADMM is applied to network optimization problems, where we optimize the pressure drop across a surface choke for every well to maximize oil production. In a large-scale network case with over 1000 wells, we achieve 2X – 3X speedups in computation time with reasonable accuracy from the ADMM framework compared with benchmarks. Finally, we apply the proposed method to a field case, and validate that the ADMM framework properly works for the actual field applications. A novel framework for surface network optimization was developed using the distributed agent optimization algorithm. The proposed framework provides superior computational efficiency for large- scale network optimization problems compared with existing benchmark methods. It enables more efficient and frequent decision-making of large-scale petroleum field management to maximize the hydrocarbon production subject to numerous system constraints.