| 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. |
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Meeting Times:
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TBA |
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Team Size:
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6
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| Open Spots: | 0 |
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Special Opportunities:
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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
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Team Needs:
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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. |
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Description:
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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 |