A data-driven workflow for predicting horizontal well production using vertical well logs

In recent work, data-driven sweet spotting technique for shale plays previously explored with vertical wells has been proposed. Here, we extend this technique to multiple formations and formalize a general data-driven workflow to facilitate feature extraction from vertical well logs and predictive m...

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Published in:arXiv.org 2017-05
Main Authors: Guevara, Jorge, Kormaksson, Matthias, Zadrozny, Bianca, Lu, Ligang, Tolle, John, Croft, Tyler, Wu, Mingqi, Limbeck, Jan, Hohl, Detlef
Format: Article
Language:English
Subjects:
Feature extraction
Horizontal wells
Kriging
Mathematical models
Workflow
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container_title arXiv.org
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creator Guevara, Jorge
Kormaksson, Matthias
Zadrozny, Bianca
Lu, Ligang
Tolle, John
Croft, Tyler
Wu, Mingqi
Limbeck, Jan
Hohl, Detlef
description In recent work, data-driven sweet spotting technique for shale plays previously explored with vertical wells has been proposed. Here, we extend this technique to multiple formations and formalize a general data-driven workflow to facilitate feature extraction from vertical well logs and predictive modeling of horizontal well production. We also develop an experimental framework that facilitates model selection and validation in a realistic drilling scenario. We present some experimental results using this methodology in a field with 90 vertical wells and 98 horizontal wells, showing that it can achieve better results in terms of predictive ability than kriging of known production values.
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subjects Feature extraction
Horizontal wells
Kriging
Mathematical models
Workflow
title A data-driven workflow for predicting horizontal well production using vertical well logs
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