Cross-well 4-D resistivity tomography localizes the oil–water encroachment front during water flooding

Zhang, J.; Revil, A.

doi:10.1093/gji/ggv028

Cited by 7 publications

(6 citation statements)

References 45 publications

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“…At small times the function does not change noticeably, but at later times it begins to vary in proportion to t −2.5 . Let us focus on the capability of the proposed computational method for the inverse Sumudu transformation of function (38). We define a set of points 𝑡 and 𝑢 through (35).…”

Section: Computational Experimentsmentioning

confidence: 99%

“…Having the set of points 𝑡 and 𝑢 , we generate the matrix and the right-side vector of linear system (34). To solve this system, we resort to Let us focus on the capability of the proposed computational method for the inverse Sumudu transformation of function (38). We define a set of points t i and u i through (35).…”

Section: Computational Experimentsmentioning

confidence: 99%

“…where f (t) is the exact function, f δ (t) is an approximation of the function f (t). Now, move on to the influence of the noise level δ in the Sumudu image (38) on the relative error of inverting the Sumudu image with the proposed method. Figure 2 illustrates the dependence of the relative error ν(t) on time and on noise level for TEM signals at a distance between the loops of 100 m and the lower half-space conductivity of 0.1 S/m.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…Currently, the most ubiquitous approach towards permafrost monitoring is borehole temperature time-lapse surveys [35][36][37]. Next in abundance are geoelectric prospecting techniques, including their cross-borehole modifications and 3D data inversion: electrical resistivity tomography [38][39][40][41] and ground penetrating radar [42][43][44]. There exist experimental works that simulate resistivity monitoring of the active permafrost layer in laboratory conditions [45].…”

Section: Introductionmentioning

confidence: 99%

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Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks

Glinskikh,

Nechaev,

Mikhaylov

et al. 2024

Mathematics

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Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. We propose a cross-borehole exploration system for a high localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We consider numerical simulation results of the transient electromagnetic monitoring of industrial facilities located on permafrost. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.

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Section: Computational Experimentsmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

Section: Computational Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks

Glinskikh,

Nechaev,

Mikhaylov

et al. 2024

Mathematics

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“…A logical extension of paired (difference) inversion is the introduction of a temporal regularization scalar which allows smoothing across any number of data sets (e.g., Kim, Yi, Park, & Kim, 2009). Such an approach then results in the simultaneous inversion of multiple data sets (now commonly referred to as 4D inversion for time‐lapse 3D resistivity), which is gaining in popularity (e.g., Uhlemann et al, 2017; Zhang & Revil, 2015) and may prove valuable for processing LTRM data sets.…”

Section: Developments In Approaches For Data Analysismentioning

confidence: 99%

Advancing hydrological process understanding from long‐term resistivity monitoring systems

Slater

Binley

2021

WIREs Water

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Monitoring subsurface flow and transport processes over a wide range of spatiotemporal scales remains one of the greatest challenges in hydrology. Electrical geophysical techniques have been implemented to noninvasively investigate a broad range of subsurface hydrological processes. Recent advances in instrumentation and interpretational tools highlight the emerging opportunities to adopt long‐term resistivity monitoring (LTRM) to improve understanding of flow and transport processes operating over monthly to decadal timescales that are not adequately captured in short‐term monitoring data sets and are temporally aliased in data sets constructed from occasional reoccupation of a study site. The emergence of LTRM as a robust tool in hydrology represents a paradigm shift in geophysical data acquisition and analysis, with resistivity monitoring now evolving into a hydrological decision support technology. We describe the theoretical basis for adopting LTRM for noninvasive monitoring of hydrological state variables over multiple spatial scales and with higher temporal resolution than achieved from periodic reoccupation of a field site. Instrumentation developments facilitating autonomous data acquisition at off the grid field sites are discussed, along with advances in data processing that enhance the hydrological information content inherent in LTRM data sets. Case studies from a diverse range of hydrology subdisciplines highlight the largely untapped potential for LTRM to provide information beyond the reach of established hydrology tools. Future opportunities and challenges relating to the more widespread adoption of LTRM, including addressing inherent uncertainty in resistivity interpretation, upscaling, computational, and modeling needs are critically discussed. This article is categorized under: Science of Water (WCAA)

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A Review of Electrical Resistivity Tomography Applications in Underground Imaging and Object Detection

Ducut

Alipio

et al. 2022

Displays

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Cross-well 4-D resistivity tomography localizes the oil–water encroachment front during water flooding

Cited by 7 publications

References 45 publications

Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks

Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks

Advancing hydrological process understanding from long‐term resistivity monitoring systems

A Review of Electrical Resistivity Tomography Applications in Underground Imaging and Object Detection

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