Logging curve restoration based on correlation information mining

Lechuan Hao; Zhimin Cao; Jian Han; Jing Li; Xiaowei Zhang

doi:10.1117/12.3019517

23 January 2024 Logging curve restoration based on correlation information mining

Lechuan Hao, Zhimin Cao, Jian Han, Jing Li, Xiaowei Zhang

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129782Y (2024) https://doi.org/10.1117/12.3019517
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

In the application of reservoir geological description using well logging data, some well logging curves are often distorted or missing. For this reason, well logging curve restoration has always been a hotspot and difficulty in related research fields. The current methods, which use the traditional signal restoration method and the restoration method based on machine learning such as neural network, do not sufficiently express and utilize the correlation information between different logging curves in the same well, and with poor cross-well adaptability. For these problems, a logging curve restoration method based on graph representation for spatio-temporal correlation information mining is proposed. The proposed method firstly describes the graph structure between different logging curves based on the commonly used graph representation learning in the field of signal processing, so that the logging curves can establish a nonlinear mapping relationship. And then, the hierarchical structure of the deep forest is introduced to realize the representation of the longitudinal information of the logging curve. Through the experimental verification of the proposed method, the prediction results of multiple well logging curves from different wellheads are obtained.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lechuan Hao, Zhimin Cao, Jian Han, Jing Li, and Xiaowei Zhang "Logging curve restoration based on correlation information mining", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129782Y (23 January 2024); https://doi.org/10.1117/12.3019517

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