Machine Learning of Core-Log Depth Matching: A Case Study in Carbonate Karst Reservoirs

Abstract

Core-log depth matching is an important part of petrophysical analysis and comprehensive logging interpretation, usually this processing is handled manually, the reasonable and robust depth correction determines the quality of the petrophysical model. Karst reservoirs exist widely at the top of Cretaceous carbonate reservoir-Mishrif Formation in the Mesopotamia Basin, Iraq. In Karst intervals, there are serious leakage during drilling, low core recovery, and bad borehole conditions would cause logging abnormally, all these unfavorable factors make it difficult to calibrate the core depth artificially. In this paper the authors present the machine learning with correlation coefficient analysis, compared the shape of the core data and the logging, verified the correction with core photos, MICP, NMR, drilling speed, core description etc., finally realize the automatic core-log depth correction, and improved the credibility of core-log correlation, and amended the data quality in the Karst intervals. Data processing shows that machine learning can effectively raise precision and efficiency of depth assignment in complex reservoirs.

Copyright 2022, IFEDC Organizing Committee

This paper was prepared for presentation at the 2022 International Field Exploration and Development Conference in Xi’an, China, 16–18 November 2022.

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