| dc.description.abstract | Abstract. Layered water injection continues to serve as a critical management technique in optimizing oilfield development, particularly in heterogeneous, multilayered reservoirs with high water content. Traditional methodologies, which often rely on computationally intensive geological modeling, face limitations in efficiently addressing the dynamic challenges of injection allocation. This study introduces an innovative data-driven strategy that leverages existing reservoir geological data and historical production records to estimate vertical and horizontal water injection allocations with enhanced precision. By circumventing the need for complex geological models, the proposed approach significantly reduces computational demands while refining injection protocols through robust analysis of historical performance metrics. Key advancements include the development of a systematic framework for calculating reservoir- and well-specific water injection ratios, coupled with an improved simultaneous perturbation stochastic approximation (SPSA) algorithm to optimize injection-recovery dynamics. Empirical validation in the Xinjiang L reservoir demonstrated notable improvements: Over a two-year implementation period in representative well groups, cumulative oil production increased by 5.98%, while cumulative water injection and well-zone water content decreased by 3.74% and 3.58%, respectively, compared to conventional practices. These results underscore the method's efficacy in enhancing injection efficiency and reservoir management, offering a scalable solution for heterogeneous multilayer systems. The study contributes to petroleum engineering by presenting a pragmatic, data-centric alternative to traditional modeling, with direct implications for reducing operational costs and extending reservoir lifecycles. This approach not only advances academic discourse on injection optimization but also provides field practitioners with a deployable strategy for sustainable resource extraction. | |
