Development of an effective completion schedule for a petroleum reservoir with strong aquifer to control water production

Abstract

AbstractsA reasonable solution, to deal with oil field water problem, is to minimize the amount of water associated with oil production using effective completion lengths. This work presents an effective method to optimize wells’ completion lengths in an oil reservoir with a strong aquifer. The suggested technique is formulated as a constrained optimization problem that defines a NPV objective function and a set of existing field/facility constraints. An effective algorithm translates the completion lengths to connections number in the dynamic simulation model. In this approach, a genetic algorithm (GA), an adaptive version of simulated annealing (ASA) and a particle swarm optimization (PSO) hybridized with polytope technique are applied to maximize NPV. A comparison is given for their performances in a strong water-drive reservoir where the combinatorial effects of wells’ completion lengths (decision variables) should be addressed. Optimizing the lengths of completions leads to an increased production period, total oil production, retarding water breakthrough, reducing total water production, and finally increasing ultimate recovery. The results showed that total oil production by GA, ASA and PSO algorithm is increased by 11.0%, 2.40% and 2.22%, respectively, related to the initial case. Total water productions are decreased by GA, 9.82%, by ASA 2.11%, and by PSO 1.82% relative to the initial schedule. The best performance belongs to the GA algorithm. Moreover, the average watercut of all wells is decreased through the optimization process. Besides, based on the numerical simulation, closing the worst connections with high watercut decreases total water production, and improves oil recovery, maximum well productivity, and NPV (oil–water ratio is increased 18.2%). Most connections are placed in the layers where water coning can occur later (considering near-well-bore permeability) and slightly far from full water zone.

A New Method for Optimizing Water-Flooding Strategies in Multi-Layer Sandstone Reservoirs

“…Moreover, some new methods, such as those using big data and artificial intelligence, have also been introduced into the optimization of water injection. For example, a combination of the hybrid genetic algorithm, particle swarm optimization (PSO), and streamline-based reservoir simulation (Naderi, 2021 [13]; Azamipour, 2018 [14], 2023 [15]) has been studied. Furthermore, the reservoir is also approximated as a multi-well injection-production system, and capacitance and resistant models (Yousef, 2006 [16]; Soroush, 2014 [17]; Yousefi, 2020 [18]; Huang, 2023 [19]; Guo, 2023 [20]), system analysis models (Liu, 2009 [21]), and INSIM-derived models (Zhao, 2014 [22], 2016 [23], 2019 [24], 2022 [25]) have been developed to predict and optimize inter-well connectivity and the water-injection rate.…”