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Abstract

Summary

This study will guide how to integrate notable regression supervised machine learning approaches, namely multiple linear regression, decision tree regression, and random forest regression in the existing workflow of predicting future performance of drilling prospects. Expert geological insight, methodical statistical refinement, and sound data processing are synergized in a seamless workflow.

The primary objective of this project is to construct a robust workflow to predict critical responses of thousands of planned new development well in a gas condensate project. The responses of interest here are net pay and reserves per foot. These are parameters to evaluate reserves per well, which is extensively used in the prospect ranking workflow and field development plan optimization. Currently, there are thousands of existing producing wells in nearby areas with similar geologic and stratigraphic features. This well collection will serve as a training data block but requires methodical exploratory analysis and strategic statistical refinement. Key predictor variables will be deduced for each response variable

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/content/papers/10.3997/2214-4609.202132006
2021-03-08
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.202132006
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