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

Abstract

Abstract: 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 calculated 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 applied to evaluate the effectiveness of water injection in different layers of wells and to perform the water injection direction adjustment. Finally, the particle swarm algorithm is used to optimize the detailed water injection plan and to make production predictions. 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 faulted reservoir in eastern China, achieving a fitting level 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.

Keywords

• zonal water injection
• fine water injection
• evaluation index
• optimization plan
• big data
• data-driven