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What is Machine Learning for Optimal Pressure Testing about?

This paper presents a machine learning approach to optimize formation pressure testing locations, improving the quality and efficiency of reservoir evaluations. By analyzing data from 23 wells, the study demonstrates a reduction in unsuccessful pretest locations from 43% to between 15% and 5%, leading to significant time savings. The methodology utilizes various machine learning algorithms to correlate conventional logging data with pressure test quality, ultimately guiding engineers in selecting optimal te

Author

Marcelo Abb

Language

EN