Research on Prediction Platform and Application of Water-Driven State Based on System Modeling Algorithm

Abstract

With the continuous expansion of water drive oil fields and the increasing difficulty of oil recovery, the accuracy requirements for predicting water drive performance are also increasing. Based on this, this article utilizes system modeling algorithms to construct a high-precision water drive dynamic prediction platform, and conducts model validation and application research on actual oil production data, achieving certain successful results. This platform has built a complete, efficient, and operable water drive dynamic prediction method, which can accurately and stably predict the oil, water, and gas production of water drive oil recovery, achieving the goal of real-time monitoring of oil production wells. In practical applications, the platform effectively solves the problem of predicting water drive production capacity in oil production wells by combining data regression analysis and grey theory. Therefore, the research on the system modeling algorithm based water drive dynamic prediction platform and its application will provide valuable and practical references for the production management and sustainable development and management of resource oceans in the oilfield field.