AI/machine learning

Study Looks at Well Construction as Engineering With Data

The authors make the case that data science captures value in well construction when data-analysis methods, such as machine learning, are underpinned by first principles derived from physics and engineering and supported by deep domain expertise.

At the highest level, there are links from a data “trunk” to all data for eachwell. This enables complex data analyses across the full set of data available for eachwell, and all of the data associated with each well is preserved and locatable by any user.
At the highest level, there are links from a data “trunk” to all data for eachwell. This enables complex data analyses across the full set of data available for eachwell, and all of the data associated with each well is preserved and locatable by any user.
Source: SPE 217665.

The authors write that data science captures value in well construction when data-analysis methods such as machine learning are underpinned by first principles derived from physics and engineering and supported by deep domain expertise. The authors provide a summary of important data pertaining to the well-construction process and discuss high-level areas where data science can add value to well construction through analysis of such data.

Introduction

The authors write that it is important for organizations to grow data science and physics-based modeling solutions together; well-construction data science should be what they term an “engineering with data” endeavor.

A large portion of the complete paper is dedicated to discussing ways that organizations can improve their abilities to derive value from data-science efforts. Most discussion focuses on steps that data science teams can take today. However, the provided commentary on data management and governance is forward-looking.

The paper concludes by examining several historical data-science case studies for well construction.

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