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Optimize Well Spacing through Predictive Analytics and Automated Data Management

Integrated Well Spacing Solution by IHS Markit

Our well spacing workflow is a completely connected solution that improves analysis by greatly reducing risk. It helps Shale Operators:

  • Improve operational efficiency by up to 15-25% through transparency and collaboration among technical, strategic and financial teams and by automating data management;
  • Optimize well placement by linking geology and engineering scenarios to macro- and regional market signals as well as actual well, completion and production results;
  • Visualize (via heat map) high probability prospective drilling areas indicating the optimal spacing required to avoid frac hits and/or interference; and
  • Reduce interpretation bias by utilizing a high-quality data foundation as the single ‘source of truth’ across multiple disciplines.

Solution Overview

The IHS Markit Well Spacing workflow produces a heat map of the most favorable locations for new wells based on:

  • Available spacing with no interference to existing neighboring wells;
  • Most applicable ‘recipes’ for cost-effective drilling and completion strategies;
  • Actual production volumes per formation;
  • Proximity to pipelines;
  • Macro short- and long-term supply and demand predictions; and
  • Geologic setting.


By integrating disparate data sources, we maintain a ‘single source of truth’ methodology that reduces interpretation bias and improves decision-making. The entire organization can understand the assumptions utilized to create the heat map and can rapidly modify or incorporate new information as it becomes available.

Additionally, because of our unique modeling capabilities, predictive analytics, data automation and AI functionality, Operators can visualize and analyze large regional swaths of 100’s wells rather than working on multiple smaller projects of 10-15 wells at a time. This provides significant time savings, but more importantly reduces interpretation bias by providing a more holistic understanding of the entire opportunity vs. piecing together multiple smaller analyses which could introduce data discrepancies and variations.

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