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Fall 2020: Data Driven Insights for Oil & Gas Drilling Operations

Affiliations: Petroleum Engineering Department, Interdisciplinary Engineering Department, Mechanical Engineering Department, Industrial Engineering Department, DeBakey Research Leadership Program
Project Leader: Narendra Vishnumolakala
vnaren@tamu.edu
Petroleum Engineering
Faculty Mentor: Dr. Eduardo Gildin, Ph.D.
Meeting Times:
TBA
Team Size:
6
Open Spots: 0
Special Opportunities:
A great opportunity to work with real-world data to solve practical issues in the O&G industry. Opportunity to learn and apply Machine Learning concepts. This work could potentially be presented at Student Paper Contest. In addition, team members who made significant contribution will obtain co-authorship on publications that may result from this work
Team Needs:
Potential team members should have at least a beginner level expertise in Python programming. Understanding of O&G drilling operations is a plus. Students will perform activities like drilling data preprocessing, exploratory analysis and implementation of machine learning models as appropriate. Students will analyze model results and provide recommendations based on analysis.
Description:
Develop models based on supervised machine learning with real-world surface and downhole drilling data to solve real-time drilling issues like dysfunction detection. In addition to dysfunction detection, models for other scenarios like trajectory optimization, borehole management, etc can be developed. Pattern recognition and data analytics on the real-time data will throw light on performance limiters that can be utilized in future bit redesigning for example

 

Written by:
Elif Kilicarslan
Published on:
June 9, 2020

Categories: FullTags: Fall 2020

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