This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206267, “Benchmarking and Field Testing of the Distributed Quasi-Newton Derivative-Free Optimization Method for Field Development Optimization,” by Faruk Alpak, SPE, Yixuan Wang, SPE, and Guohua Gao, SPE, Shell, et al. The paper has not been peer reviewed.
Recently, a novel distributed quasi-Newton (DQN) derivative-free optimization (DFO) method was developed for generic reservoir-performance optimization problems, including well-location optimization (WLO) and well-control optimization (WCO). DQN is designed to locate multiple local optima of highly nonlinear optimization problems effectively. However, its performance has been neither validated by realistic applications nor compared with other DFO methods. Field-testing results reinforce the auspicious computational attributes of DQN.
An optimization problem is posed as the minimization or maximization of an objective function by modifying the control variables (x) within a search domain. The objective function is a highly nonlinear function of x and may have multiple local optima. In this paper, the objective function is assumed to be twice differentiable.
DFO methods can be classified into local search methods and global search methods. In the complete paper, the authors’ goal is to locate multiple local optima of the objective function. Their focus is on local search DFO optimization methods, which include direct search methods and model-based methods.
Current DFO methods reviewed in the complete paper have a common feature: Only one best approximation to the solution is updated in each iteration, and only one optimal solution is identified in the last iteration. Therefore, they are referred to as single-thread DFO methods. They do not represent an efficient approach because simulation results obtained by one optimization task starting from one initial guess are not shared with other optimization tasks that start from different initial guesses. The distributed Gauss-Newton method and the DQN method benchmarked in the paper are referred to as multiple-thread optimization methods.
The authors have integrated DQN into a versatile field-development optimization platform designed specifically for iterative work flows enabled through distributed-parallel flow simulations.