Resistivity Logging Through Casing Response of Inclined Fractured Formation

Liu, Xiaobo; Liu, Fuping; Chen, Jingyi; Zhao, Zhencong; Wang, Anling; Lu, Zhipeng

doi:10.1109/tgrs.2018.2842641

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…With the development of array imaging, micro-resistivity array imaging system [24][25][26] can obtain a variety of rich formation information in circumferential direction and radial direction of the borehole, which is suitable for the exploration and development of complex concealed oil and gas reservoirs [27][28][29]. One of the most widely used micro-resistivity imaging tool is the formation micro scanner, which was developed by Schlumberger in the 1980s.…”

Section: Introductionmentioning

confidence: 99%

A Universal Multi-Frequency Micro-Resistivity Array Imaging Method for Subsurface Sensing

Yang

Liu

et al. 2022

Remote Sensing

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In this paper, a universal multi–frequency micro-resistivity array imaging (UMMAI) system for subsurface sensing is developed and verified. Different from conventional micro-resistivity imaging equipments, UMMAI is capable to provide high-resolution fullbore formation images in multiple logging environments including an oil-based mud scene, water-based mud scene and water-oil mixed mud scene, owning to the large dynamic range and good linearity of transceivers. With the advantage of diversity in excitation signal frequency, UMMAI presents abundant amplitude–frequency characteristics response images and phase–frequency characteristics response images of subsurface formations at the same time, which is beneficial to multi–frequency image fusion in the future. The fullbore imaging ability of UMMAI is evaluated in three different field tests, and the results show that UMMAI can give satisfactory credible formation images with high resolution, which is suitable for subsurface formation discrimination and useful for reservoir identification.

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Section: Introductionmentioning

confidence: 99%

A Universal Multi-Frequency Micro-Resistivity Array Imaging Method for Subsurface Sensing

Yang

Liu

et al. 2022

Remote Sensing

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“…E lectrical resistivity survey (ERS), as one of the most wellknown and commonly applied investigation technologies (Loke et al [1]), has been widely used in environmental investigations (LaBrecque et al [2], Hayley et al [3], Reynolds [4]), engineering prospecting (Kim et al [5], Wilkinson et al [6], Sjodahl et al [7]), hydrological surveys (Park [8]; Slater and Binley [9], Rucker [10], Cho et al [11]) and mining applications (Legault et al [12], Chambers et al [13], Liu et al [14]). Geological interpretations using observed data are usually far from revealing the complex characteristics of subsurface properties.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Inversion of Electrical Resistivity Data

Liu

Guo

et al. 2020

IEEE Trans. Geosci. Remote Sensing

138

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The inverse problem of electrical resistivity surveys (ERS) is difficult because of its nonlinear and ill-posed nature. For this task, traditional linear inversion methods still face challenges such as sub-optimal approximation and initial model selection. Inspired by the remarkable non-linear mapping ability of deep learning approaches, in this paper we propose to build the mapping from apparent resistivity data (input) to resistivity model (output) directly by convolutional neural networks (CNNs). However, the vertically varying characteristic of patterns in the apparent resistivity data may cause ambiguity when using CNNs with the weight sharing and effective receptive field properties. To address the potential issue, we supply an additional tier feature map to CNNs to help it get aware of the relationship between input and output. Based on the prevalent U-Net architecture, we design our network (ERSInvNet) which can be trained endto-end and reach real-time inference during testing. We further introduce depth weighting function and smooth constraint into loss function to improve inversion accuracy for the deep region and suppress false anomalies. Four groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed methods. According to the comprehensive qualitative analysis and quantitative comparison, ERSInvNet with tier feature map, smooth constraints and depth weighting function together achieve the best performance.

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Electric field and potential distribution in layered homogeneous borehole fluids and its logging responses of resistivity logging through casing

Chen

2023

Geoenergy Science and Engineering

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Resistivity Logging Through Casing Response of Inclined Fractured Formation

Cited by 9 publications

References 20 publications

A Universal Multi-Frequency Micro-Resistivity Array Imaging Method for Subsurface Sensing

A Universal Multi-Frequency Micro-Resistivity Array Imaging Method for Subsurface Sensing

Deep Learning Inversion of Electrical Resistivity Data

Electric field and potential distribution in layered homogeneous borehole fluids and its logging responses of resistivity logging through casing

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