Numerical Modelling of Borehole-Surface CSEM Response of Onshore Gas Hydrate Deposit with Higher Order Finite Difference Method

Omisore, Busayo Oreoluwa; Jin, Sheng; Fayemi, Olalekan

doi:10.1016/j.jappgeo.2020.103968

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…In this sense, the CSEM has become more and more popular in many different application scenarios due to its ability to display conductivity contrasts with respect to their surrounding sediments (e.g., detecting resistive zones in a conductive background). As a result, nowadays CSEM has real application in many contexts such as hydrocarbon exploration [1]- [10], reservoir monitoring [11], [12], CO 2 storage characterization [13]- [18], and geothermal reservoir imaging [19]- [22], among others.…”

Section: Introductionmentioning

confidence: 99%

Land CSEM Simulations and Experimental Test Using Metallic Casing in a Geothermal Exploration Context: Vallès Basin (NE Spain) Case Study

Castillo-Reyes

Queralt

Marcuello

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Controlled-source electromagnetic (CSEM) measurements are complementary data for magnetotelluric (MT) characterization, although its methodology on land is not sufficiently developed and tested as in marine environments. Acquiring expertise in CSEM is crucial for surveys in places where MT cannot be performed due to high-levels of cultural noise. To acquire that expertise, we perform CSEM experiments in the Vallès fault (Northeast (NE), Spain) where MT results have been satisfactory and allow us to verify the CSEM results. The Vallès basin is relevant for potential heat generation because of the presence of several geothermal anomalies, and its nearby location to urban areas. In this paper, we present the experimental setup for that region, a 2-D joint MT+CSEM inverse model, several 3-D CSEM simulations in the presence of metallic casing, and its comparison with real data measurements. We employ a parallel and high-order vector finite element algorithm to discretize the governing equations. By using an adapted meshing strategy, different scenarios are simulated to study the influence of the source position/direction and the conductivity model in a metallic casing presence. An excellent agreement between simulated data and analytical/real field data demonstrates the feasibility of study metallic structures in realistic configurations. Our numerical results confirm that metallic casing strongly influences electromagnetic responses, making surface measurements more sensitive to resistivity variations near the metallic structure. It could be beneficial getting higher signal-to-noise ratios and sensitivity to deep targets. However, such casing effect depends on the input model (e.g., conductivity contrasts, frequency, and geometry).

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Section: Introductionmentioning

confidence: 99%

Land CSEM Simulations and Experimental Test Using Metallic Casing in a Geothermal Exploration Context: Vallès Basin (NE Spain) Case Study

Castillo-Reyes

Queralt

Marcuello

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Since EM methods can display resistivity/conductivity variations concerning their surrounding materials, EM imaging has greatly complemented seismic methods (sensitive to subsurface structure). As a result, EM imaging technologies have been applied in different areas of geophysics such as off-shore hydrocarbon (Newman and Alumbaugh, 1997;Eidesmo et al, 2002;Avdeev, 2005;Constable, 2006;Srnka et al, 2006;Orange et al, 2009;Constable, 2010;Wirianto et al, 2010;Castillo-Reyes et al, 2018;Castillo-Reyes et al, 2019;Castillo-Reyes et al, 2022b), mineral and resource mining (Sheard et al, 2005;Yang and Oldenburg, 2012), crustal conductivity studies (Hördt et al, 1992;Hördt et al, 2000), CO 2 storage characterization (Chen et al, 2007;Girard et al, 2011;Vilamajó et al, 2013;Zhdanov et al, 2013;Vilamajó et al, 2016;Park et al, 2017;Tveit et al, 2020), geothermal reservoir imaging (Kana et al, 2015;Coppo et al, 2016;Darnet et al, 2018;Omisore et al, 2020;Castillo-Reyes et al, 2021), and water prospecting (Palacky et al, 1981;McNeill, 1990;Nabighian and Macnae, 1991;Palacky, 1993;Nobes, 1996;Chang et al, 2019). Regardless of the application context, the authors stand out on the potential of EM methods for exploration, appraisal, and soil characterization.…”

Section: Electromagnetic Data For Enhanced Imaging Of the Earthmentioning

confidence: 99%

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Castillo-Reyes,

Hu,

Wang

et al. 2023

Front. Earth Sci.

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Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.

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Marine CSEM 3D modeling with a downhole dipole source for natural gas hydrate production monitoring

Liu

et al. 2022

Mar Geophys Res

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Numerical Modelling of Borehole-Surface CSEM Response of Onshore Gas Hydrate Deposit with Higher Order Finite Difference Method

Cited by 5 publications

References 12 publications

Land CSEM Simulations and Experimental Test Using Metallic Casing in a Geothermal Exploration Context: Vallès Basin (NE Spain) Case Study

Land CSEM Simulations and Experimental Test Using Metallic Casing in a Geothermal Exploration Context: Vallès Basin (NE Spain) Case Study

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Marine CSEM 3D modeling with a downhole dipole source for natural gas hydrate production monitoring

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