Time-lapse resistivity imaging: CSEM-data 3-D double-difference inversion and application to the Reykjanes geothermal field

Bretaudeau, François; Dubois, Frédéric; Kassa, S-G Bissavetsy; Coppo, Nicolas; Wawrzyniak, Pierre; Darnet, Mathieu

doi:10.1093/gji/ggab172

Cited by 14 publications

(7 citation statements)

References 27 publications

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“…One of the first EM imaging attempts for geothermal applications dates to the 1980s/1990s, when Ward (1983) and Thanassoulas (1991) investigated synthetic EM responses for typical-form geothermal sites. Later, diverse and several impressive studies demonstrated the value of EM data sets for the detection, characterization, and monitoring of 2D/3D geothermal reservoirs (Spichak and Manzella, 2009;Streich et al, 2010;Peacock et al, 2013;Munoz, 2014;Bretaudeau et al, 2017;Didana et al, 2017;Darnet et al, 2019;Bretaudeau et al, 2021). All the aforementioned studies showed that EM surveys provide complementary and valuable information for the assessment of geothermal resources.…”

Section: Geothermal Reservoirsmentioning

confidence: 98%

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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Section: Geothermal Reservoirsmentioning

confidence: 98%

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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“…In this part, we describe the inversion framework proposed to infer a 3-D CR model from EM data. Our work relies on the implementation of CR modelling and inversion into the code POLYEM3D (Bretaudeau et al 2021). This code is a 3-D finite-difference parallel code for modelling and inversion of CSEM and MT data in the frequency domain.…”

Section: N V E R S I O N O F C O M P L E X R E S I S T I V I T Ymentioning

confidence: 99%

“…They were intensively used for imaging hydrocarbon reservoirs in marine applications (Constable 2010). They were also used to image and monitor geothermal reservoirs (Darnet et al 2020;Bretaudeau et al 2021). The CSEM methods have a higher depth of investigation than the DC method.…”

Section: Introductionmentioning

confidence: 99%

3-D complex resistivity imaging using controlled source electromagnetic data: a multistage procedure using a second order polynomial parametrization

Porté

Bretaudeau

Girard

2022

Geophysical Journal International

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Summary In some Earth materials, significant induced polarization (IP) phenomena are occurring when an electric perturbation is applied. These mechanisms are described by a frequency-dependent complex resistivity (CR). The study of the CR spectral signature allows to access indirectly to several properties of interest of the subsurface linked to the interaction between the pore space and fluids. CR is usually studied using the electrical method with a direct current approximation, neglecting by the way electromagnetic (EM) induction that can occur in the data. However, EM induction increases with frequency and offset, resulting in limitations at high frequencies or for the investigation of deep target. We implemented a frequency-dependent CR in a 3D finite-differences (FD) modelling and inversion code for frequency domain controlled-source electromagnetic (CSEM) data to take into consideration IP information contained in EM data or reciprocally. The CSEM methods are resistivity imaging techniques using multi-frequency EM fields that fully take into account EM induction with large investigation depth. Following a preliminary sensitivity study, a multi-stage inversion framework was designed to constrain the multi-parameter inverse problem. Furthermore, to manage the increasing number of parameters, a second-order polynomial parameterization is used to describe independently frequency variation of CR norm and phase. We demonstrate the method through 1D and 3D synthetic data inversions for a deep-target model. We show that we were able to recover the CR and its frequency variation from CSEM data in the IP/EM coupling domain for 1D targets. The problem of deep polarizable 3D targets is more challenging and the resolution of the recovered CR spectrum was impacted. Nevertheless, we retrieved from a model containing several polarizable anomalies some crucial information allowing the discrimination of the targets from the non-polarizable background and from different spectral CR signatures. Our inversion strategy allows thus accessing to IP parameters of the medium in an extended frequency domain by fully taking EM induction information into account.

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“…TL strategies can be roughly divided into three categories: (i) difference inversion (LaBrecque and Yang 2001;Ajo-Franklin et al 2007;Carbajal et al 2012;Bretaudeau et al 2021) and ratio inversion (Daily et al 1992), which take the difference/ratio of the observed data as data input and invert for model differences, allowing an efficient suppression of the systematic noise and lowering 3D time-lapse TEM inversion 585 of the computational dimensionality; (ii) cascaded inversion (Oldenborger et al 2007;Miller et al 2008;McLachlan et al 2020), which inverts the data sets sequentially based on the previous inversion result and (iii) simultaneous inversion of two (or multiple) data sets with constrained models (Kim et al 2009;Hayley et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional time-lapse inversion of transient electromagnetic data, with application at an Icelandic geothermal site

Xiao

Fiandaca

Maurya

et al. 2022

Geophysical Journal International

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Summary Transient electromagnetic (TEM) is an efficient non-invasive method to map electrical conductivity distribution in the subsurface. This paper presents an inversion scheme for three-dimensional (3D) TEM time-lapse data using a generalized minimum support norm and its application to monitoring conductivity changes over time. In particular, two challenges for time-lapse TEM applications are addressed: i) the survey repetition with slightly different acquisition position, i.e. because systems are not installed; ii) non-optimal data coverage above the time-lapse anomalies, for instance, due to the presence of infrastructure that limits the acquisition layout because of coupling. To address these issues, we developed a new TEM time-lapse inversion scheme with the following features: (1) a multi-mesh approach for model definition and forward computations, which allows for seamless integration of datasets with different acquisition layouts; (2) 3D sensitivity calculation during the inversion, which allows retrieving conductivity changes in-between TEM soundings; (3) simultaneous inversion of two datasets at once, imposing time-lapse constraints defined in terms of a generalized minimum support norm, which ensures compact time-lapse changes. We assess the relevance of our implementations through a synthetic example and a field example. In the synthetic example, we study the capability of the inversion scheme to retrieve compact time-lapse changes despite slight changes in the acquisition layout and the effect of data coverage on the retrieval of time-lapse changes. Results from the synthetic tests are used for interpreting field data, which consists of two TEM datasets collected in 2019 and 2020 at the Nesjavellir high-temperature geothermal site (Iceland) within a monitoring project of H2S sequestration. Furthermore, the field example illustrates the effect of the trade-off between data misfit and time-lapse constraints in the inversion objective function, using the tuning settings of the generalized minimum support norm. Based on the results from both the synthetic and field cases, we show that our implementation of 3D time-lapse inversion has a robust performance for TEM monitoring.

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Time-lapse resistivity imaging: CSEM-data 3-D double-difference inversion and application to the Reykjanes geothermal field

Cited by 14 publications

References 27 publications

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

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

3-D complex resistivity imaging using controlled source electromagnetic data: a multistage procedure using a second order polynomial parametrization

Three-dimensional time-lapse inversion of transient electromagnetic data, with application at an Icelandic geothermal site

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