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Tuesday, October 15 • 11:15am - 11:30am
Automated Formation Top Labeling and Well Depth Matching by Machine Learning

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Depth matching of multiple logging curves is essential to any well evaluation or reservoir characterization and can be applied to various measurements of a single or multiple logging curves from multiple wells within the same field. As many drilling advisory projects have been launched to digitalize the well log analysis, accurate depth matching becomes an important factor in improving well evaluation, production, and recovery. It is a challenge, though, due to the unpredictable structure of the geological formations. We conduct a study on the alignment of multiple gamma-ray well logs by using machine learning techniques. The objective is to automate the depth matching task with minimum human intervention. A novel multitask learning approach is presented to optimize the depth matching strategy that correlates gamma-ray logs. The proposed approach can be extended to other applications as well, such as automatic formation top labeling for an ongoing well given a reference well.


Shirui Wang

Presenter, University of Houston

Qiuyang Shen

University of Houston

Xuqing Wu

University of Houston

Jiefu Chen

University of Houston

Tuesday October 15, 2019 11:15am - 11:30am CDT
BRC 103

Attendees (1)