Automated WAG Schedule Optimization

Published at Mar 1, 2019

Value Added: Saved >4 hours per week via an automated pipeline, better facility balancing, and prioritization of high value targets
Skills Used: Python, Optimization, Reservoir Engineering, Data Engineering
#Optimization#Genetic Algorithms#Reservoir Engineering#Automation

Summary

This project delivered a custom optimization solution for generating water alternating gas (WAG) schedules to convert injector wells between water and gas injection. Leveraging methodologies similar to genetic algorithms, the optimizer automated the process of identifying optimal swaps, replacing manual data gathering and analysis with a robust data pipeline. The approach enabled rapid evaluation of conversion scenarios, balancing injection streams and prioritizing wells with higher value for gas injection.

Project Highlights

  • Designed and implemented a custom optimizer inspired by genetic algorithms to automate WAG schedule generation
  • Replaced manual, time-consuming data gathering and swap analysis with an automated pipeline
  • Balanced injection streams across the field, ensuring operational stability and maximizing value from gas injection
  • Incorporated constraints to avoid excessive gas injection in small geographical areas, mitigating rapid GOR increases in nearby producers
  • Enabled the water and EOR flood coordinator to quickly identify and execute high-value injector conversions

Technical Innovation

  • Developed a flexible optimization framework capable of evaluating multiple swap scenarios and constraints
  • Automated data integration and scenario analysis, reducing manual effort and improving decision speed
  • Considered complex field interactions, including the impact of multiple gas injectors on producer GOR and competitiveness

Impact

The automated WAG schedule optimizer transformed the injector conversion workflow, enabling faster, data-driven decisions and reducing manual analysis time. By prioritizing high-value gas injection and maintaining balanced streams, the solution improved field performance and operational efficiency while minimizing negative impacts on producer wells.

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