The waterflooding implementation in an Amazonian oil field has been a game-changer in the field development strategy, becoming the main production drive mechanism and investment focus. About 40% of the daily oil production comes from waterflooding projects. Hence, it is imperative to preserve integrated reservoir and field operation management through a customized pattern balancing methodology that accounts for a need to optimize the injection-extraction relationship minimizing early water breakthrough and avoiding operational issues.

This article presents a waterflooding pattern analysis tool that combines data-driven and physics-based Machine Learning models with a smart optimization workflow. This publication focuses on the theoretical foundation of the deployable prototype, which is based mainly on the application of an innovative physics data driven and ML model as well as its testing procedure. The tool has been tested in an area with nine deviated water injector wells and thirty-six deviated/horizontal producer wells, enabling quick analysis response based on different What-If and optimization scenarios. Users can assess the impact on production and waterflooding response by modifying operational parameters such as injection rates or liquid flow rates, or how to react if an oil-producing/water-injection well fails.

The engineering and operation teams use and share a tool that avoids personalized spreadsheets with off-dated information and non-auditable metrics behind the results. The data preparation capabilities of the new tool speed up the interaction of data-driven and physics models and make a more efficient data flow process integrated with Capacitance Resistance Model (CRM) (Yousef et al. 2005) analytic model. The teams experienced a step-change in productivity by reducing a complete iteration analysis from 23 to 5 hours. The optimization workflow generates possible injector-producer relationships for pattern analysis and short (weekly) and mid-term (90-day) forecasts. Users can test different scenarios, choose the optimum, and submit subsurface focused well-operating recommendations to field operations.