大数据驱动下的老油田精细注水优化方法

Data-driven optimization for fine water injection in a mature oil field

在传统的数值模拟及优化算法基础上,结合分层注采实时监测与自动控制工艺技术所监测的“硬数据”,提出了一套大数据驱动下的精细注水方案优化方法。首先在动态观测数据的约束下,通过数据同化算法实现地质模型参数的自动拟合;根据分层注采流动关系自动识别方法计算区块分层注采井间的流动关系;采用多层多向产量劈分技术计算采油井分层分方向的产液量与产油量,量化注水效果指标;进一步通过机器学习算法评价多井分层的注水效果、分析注水调整方向;通过智能优化算法求解最优注水调整方案,进行产量预测。该方法和流程充分利用了数据驱动和机器学习算法的自动化、智能化优势,应用于中国东部某复杂断块油藏,数据模拟的拟合率可达85%,示例区块优化后12个月内的累计产油量与未优化时相比增加8.2%,能够精准指导老油田精细注水方案的设计和实施。

Based on the traditional numerical simulation and optimization algorithms,in combination with the layered injection and production'hard data'monitored at real time by automatic control technology,a systematic approach for detailed water injection design using data-driven algorithms is proposed.First the data assimilation technology is used to match geological model parameters under the constraint of observed well dynamics;the flow relationships between injectors and producers in the block are worked out based on automatic identification method for layered injection-production flow relationship;multi-layer and multi-direction production splitting technique is used to calculate the liquid and oil production of producers in different layers and directions and obtain quantified indexes of water injection effect.Then,machine learning algorithms are adopted to evaluate the effectiveness of water injection in different layers of wells and find out the direction of water injection adjustment.Finally,the particle swarm algorithm is used to optimize the detailed water injection plan and predict production.This method and procedure make full use of the automation and intelligence of data-driven and machine learning algorithms.This method was used to match the data of a complex fault block reservoir in eastern China,achieving a fitting rate of 85%.The cumulative oil production in the example block for 12 months after optimization is 8.2%higher than before.This method can help design detailed water injection program for mature oilfields.