Optimized Multi-Layers Inversion Scheme for Azimuthal Resistivity Logging-While-Drilling Based on Supervised Descent Method.

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Bibliographic Details
Title:	Optimized Multi-Layers Inversion Scheme for Azimuthal Resistivity Logging-While-Drilling Based on Supervised Descent Method.
Authors:	Xu, Yongsheng¹, Li, Yuehui¹ liyuehui@xhu.edu.cn, Zheng, Junyuan¹, Sun, Xiangyang², Hao, Peng², Ren, Jie¹
Source:	Progress in Electromagnetics Research C. 2025, Vol. 162, p234-241. 8p.
Subjects:	Multilayers, Machine learning, Directional drilling, Hydrocarbon reservoirs, Geophysical well logging
Abstract:	Logging-While-Drilling (LWD) azimuthal resistivity measurements deliver critical support for geosteering in complex hydrocarbon reservoirs by acquiring real-time azimuthal responses of formation electrical properties around the borehole; the precision and efficiency of its inversion directly govern the reliability of horizontal well trajectory optimization strategies. Currently, the inversion study of azimuthal resistivity logging with drilling mainly focuses on the simplified three-layer stratigraphic model, and this simple layered model and limited stratigraphic parameter settings have been difficult to adapt to the needs of the increasingly complex geological exploitation. However, inversion of complex multilayer formations (≥ 5 strata) confronts three main challenges: high-dimensional parameterization, attenuated response sensitivity, and noise-impaired accuracy. These constraints compromise field-applicable accuracy thresholds for multilayer stratigraphic inversion. To address the above problems, in this paper, by combining the advantages of traditional inversion methods with machine learning concepts, a new optimized supervised descent inversion method is proposed for azimuthal resistivity LWD in a five-layer formation model. The data-adaptive reconstruction algorithm enhances outer formation response sensitivity. The subsequent integration of multi-matrix fusion with secondary inversion optimization further augments accuracy in field well-log inversion. Numerical simulations and downhole measurements verify the effectiveness of the proposed method, which is a field-deployable real-time inversion algorithm with higher accuracy and stronger noise immunity. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Logging-While-Drilling (LWD) azimuthal resistivity measurements deliver critical support for geosteering in complex hydrocarbon reservoirs by acquiring real-time azimuthal responses of formation electrical properties around the borehole; the precision and efficiency of its inversion directly govern the reliability of horizontal well trajectory optimization strategies. Currently, the inversion study of azimuthal resistivity logging with drilling mainly focuses on the simplified three-layer stratigraphic model, and this simple layered model and limited stratigraphic parameter settings have been difficult to adapt to the needs of the increasingly complex geological exploitation. However, inversion of complex multilayer formations (≥ 5 strata) confronts three main challenges: high-dimensional parameterization, attenuated response sensitivity, and noise-impaired accuracy. These constraints compromise field-applicable accuracy thresholds for multilayer stratigraphic inversion. To address the above problems, in this paper, by combining the advantages of traditional inversion methods with machine learning concepts, a new optimized supervised descent inversion method is proposed for azimuthal resistivity LWD in a five-layer formation model. The data-adaptive reconstruction algorithm enhances outer formation response sensitivity. The subsequent integration of multi-matrix fusion with secondary inversion optimization further augments accuracy in field well-log inversion. Numerical simulations and downhole measurements verify the effectiveness of the proposed method, which is a field-deployable real-time inversion algorithm with higher accuracy and stronger noise immunity. [ABSTRACT FROM AUTHOR]
ISSN:	19378718
DOI:	10.2528/PIERC25083001