Semester of Graduation

Summer 2019

Degree

Master of Science in Petroleum Engineering (MSPE)

Department

Craft & Hawkins Department of Petroleum Engineering

Document Type

Thesis

Abstract

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different wells drilled in an area of 2 x 4 miles. The data was collected in the same rig and contains over 12 million electronic drilling recorder data points, driller’s activity logs and well profiles. The main focus is to propose a detailed workflow to clean and process real drilling data. Once cleaned, the data can be fed into data analytics platforms and machine learning models to efficiently analyze trends and plan future well more efficiently. This roadmap will serve as a basis for drilling optimization. The objective of this work is to detail the various steps needed to prepare field drilling data for business analysis, as well discuss about data analytics and machine learning application in drilling operations. The results to be presented are the detailed workflow and description of the data preparation steps, an example analysis of the drilling data and an example application of a machine learning model in drilling.

Committee Chair

Williams, Wesley Charles

DOI

10.31390/gradschool_theses.4952

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