Hydrocarbon Recovery Optimization

Situation

A major oil and gas company faced significant challenges in optimizing field development strategies across its vast acreage. Generating accurate production forecasts required months of manual effort, relying heavily on traditional type curve methods that were often inaccurate and unable to capture well-level variations. This led to delayed decisions, suboptimal development plans, and missed opportunities in competitive markets. The company partnered with Mu Sigma to transform its forecasting and planning processes through advanced data-driven insights.

Challenge

The Well Precision team struggled with the inefficiencies of traditional forecasting approaches. These methods relied on small datasets limited to existing wells, making it nearly impossible to accurately predict hydrocarbon recovery in unexplored regions. This resulted in high uncertainty in new well developments and resource allocation. The team needed a solution that could drastically reduce forecasting time, improve accuracy at the well level, and provide actionable insights to optimize development strategies

Approach

Mu Sigma employed RECOV (Reservoir Evaluation and Coordination for Optimized Value) a collaborative Machine learning platform, to revolutionize production forecasting for the Shale and Tight reseves. By integrating ML techniques with First Principles analysis, RECOV provided faster, more accurate production forecasting, surpassing the limitations of traditional methods like Type Curves. This innovative approach enabled the client to optimize well design and development strategy, address the operational challenges and improve overall efficiency

Impact

• Unlocked $2.7B in Potential Savings Over 5 Years By Enhancing Drill Queue Prioritization and Facility Sizing
• Reduced Forecast Generation Time by 99% (from ~ 6 Months to less than a day)