In this paper, a supervised machine-learning (ML) method to remove artifacts and noise from borehole images is described. Borehole images may exhibit a variety of issues and artifacts due to reasons such as environmental and thermal noise, imperfect calibration, and current leakage through the tool body. Methods that are currently employed to improve these images are based on traditional signal-processing techniques. Although these methods are capable of removing the artifacts in images and significantly improving image quality, they have some drawbacks as well. These drawbacks include not being entirely suitable for real-time implementation and issues with reproducibility. The alternative method presented here is based on an ML algorithm that is trained using a data set pairing raw data with data processed using a traditional signal-processing-based approach. The resulting ML model is capable of being implemented in near-real time. Furthermore, the application of the algorithm does not require user supervision, increasing the reproducibility of the results.

Baris Guner

Ahmed E. Fouda

Peter Barrett

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description

PETROPHYSICS 包含对地层评价的理论和应用方面的原创性贡献，包括裸眼和套管井测井、岩心分析和地层测试。

The journal "Petrophysics" publishes original research contributions on the theory and application of formation evaluation, including open-hole and cased-hole logging, core analysis, and formation testing.

《Petrophysics》期刊发表关于地层评价的原创性研究，包括裸眼和套管井测井、岩心分析及地层测试等理论与应用方面的贡献。

Category	Quartile
工程技术	4
工程技术, 工程石油	4
工程技术, 地球化学与地球物理	4

Removal of Artifacts in Borehole Images Using Machine Learning

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Journal Info

Category	Quartile
GEOCHEMISTRY & GEOPHYSICS	4