Machine Learning Workflow to Identify Brittle, Fracable and Producible Rock in Horizontal Wells Using Surface Drilling Data (original) (raw)

Day 3 Wed, October 28, 2020, 2020

Abstract

This study demonstrates the application of an interpretable (or explainable) machine learning workflow using surface drilling data to identify fracable, brittle and productive rock intervals along horizontal laterals in the Marcellus shale. The results are supported by a thorough model-agnostic interpretation of the input-output relationships to make the model explainable to users. The methodology described here can easily be generalized to real-time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production and minimizing frac hits. In practice, this information is rarely available in real-time and requires tedious and time-consuming processing of logs (including image logs), core, microseismic data and fiber optic sensor data to provide post-job validation of frac- and well-placement. Post-completion analyses are generally too late for corrective action leading to wells with a low probability of success and increasing ris...

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