[AAPG 2020] Tailoring Completions to Geology: A Machine Learning Approach

Not all rocks should receive the same completion.

It’s a simple concept, but it can be tough to back up the theory with statistical evidence. Machine learning provides a powerful tool to analyze the interactions between completions design choices and local geology.

In this talk, we analyze interactions between proppant, fluid loading, stage spacing, and geology. We then show that per-well economics can be improved by over $1MM NPV by taking a tailored approach. SHAP values (like in the image above) show what the model is thinking: what exact combinations of geology & spacing are driving these interactions.

Talk Details::

Thursday, 10:25 AM. Theme 8: Successes and Challenges on Data Management and Data Integration II


Rocks along the Nesson Anticline (brown & dark yellow) show less uplift from shortened stages than rocks in other parts of the basin, despite being overall higher-performing.

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Accurate forecast on parent-child developments

In this live webinar, you will learn how Novi’s new algorithm improves model sensitivity for spacing and parent-child scenarios, providing powerful results for previously difficult-to-analyze problems.

Ted Cross, our VP of Product Management, will show you how this update improves spacing and infill scenario analysis without sacrificing model accuracy.