[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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