Structurally-Constrained 3D Anisotropic CSEM Resistivity Inversion with Crossgradient Criterion: Proof-Of-Concept Study

Meju, Max A.; Mackie, Randall L.; Miorelli, Federico; Saleh, Ahmad Shahir

doi:10.3997/2214-4609.201801398

Cited by 2 publications

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“…Industry benchmarking in 2017 revealed inconsistencies in horizontal and vertical resistivity distributions from anisotropic 3D CSEM inversion models produced by the major service providers for a given field dataset used for the test; invited academic practitioners abstained from the test [13]. This provided the motivation for the development of a crossgradientbased anisotropic-resistivity-imaging algorithm that directly enforces structural similarity between the horizontal and vertical resistivity models without control from seismic or well data [4,14,15]. The seismic, image-guided anisotropic 3D inversion of CSEM and MT data is an adaption of this crossgradient philosophy, in which the structural controls from seismic or other images are infused in the anisotropic resistivity inversion process [7,[16][17][18].…”

Section: Figurementioning

confidence: 99%

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Using Large-Size Three-Dimensional Marine Electromagnetic Data for the Efficient Combined Investigation of Natural Hydrogen and Hydrocarbon Gas Reservoirs: A Geologically Consistent and Process-Oriented Approach with Implications for Carbon Footprint Reduction

Meju

Saleh

2023

Minerals

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The recycling or burial of carbon dioxide in depleted petroleum reservoirs and re-imagining exploration strategies that focus on hydrogen reservoirs (with any associated hydrocarbon gas as the upside potential) are a necessity in today’s environmental and geopolitical climate. Given that geologic hydrogen and hydrocarbon gases may occur in the same or different reservoirs, there will be gains in efficiency when searching for both resources together since they share some commonalities, but there is no geophysical workflow available yet for this purpose. Three-dimensional (3D) marine controlled-source electromagnetic (CSEM) and magnetotelluric (MT) methods provide valuable information on rock-and-fluid variations in the subsurface and can be used to investigate hydrogen and hydrocarbon reservoirs, source rocks, and the migration pathways of contrasting resistivity relative to the host rock. In this paper, a process-oriented CSEM-MT workflow is proposed for the efficient combined investigation of reservoir hydrocarbon and hydrogen within a play-based exploration and production framework that emphasizes carbon footprint reduction. It has the following challenging elements: finding the right basin (and block), selecting the right prospect, drilling the right well, and exploiting the opportunities for sustainability and CO2 recycling or burial in the appropriate reservoirs. Recent methodological developments that integrate 3D CSEM-MT imaging into the appropriate structural constraints to derive the geologically robust models necessary for resolving these challenges and their extension to reservoir monitoring are described. Instructive case studies are revisited, showing how 3D CSEM-MT models facilitate the interpretation of resistivity information in terms of the key elements of geological prospect evaluation (presence of source rocks, migration and charge, reservoir rock, and trap and seal) and understanding how deep geological processes control the distribution and charging of potential hydrocarbon, geothermal, and hydrogen reservoirs. In particular, evidence is provided that deep crustal resistivity imaging can map serpentinized ultramafic rocks (possible source rocks for hydrogen) in offshore northwest Borneo and can be combined with seismic reflection data to map vertical fluid migration pathways and their barrier (or seal), as exemplified by the subhorizontal detachment zones in Eocene shale in the Mexican Ridges fold belt of the southwest of the Gulf of Mexico, raising the possibility of using integrated geophysical methods to map hydrogen kitchens in different terrains. The methodological advancements and new combined investigative workflow provide a way for improved resource mapping and monitoring and, hence, a technology that could play a critical role in helping the world reach net-zero emissions by 2050.

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Section: Figurementioning

confidence: 99%

“…In the first method, the MT and CSEM data are inverted for the vertical and horizontal resistivities directly while imposing a crossgradient constraint to ensure the structural similarity between the models [14,15]. The objective function to minimize can be stated as…”

Section: Geologically Consistent 3d Anisotropic Resistivity Inversion...mentioning

confidence: 99%

Using Large-Size Three-Dimensional Marine Electromagnetic Data for the Efficient Combined Investigation of Natural Hydrogen and Hydrocarbon Gas Reservoirs: A Geologically Consistent and Process-Oriented Approach with Implications for Carbon Footprint Reduction

Meju

Saleh

2023

Minerals

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“…Constraints may also be applied to favour similarities between local variations in the vertical and horizontal models (Meju et al, 2018).…”

Section: Introduction Rationalementioning

confidence: 99%

“…Smoothing is typically done along the horizontal and vertical axes but can also be modified to follow horizons or seismic events (Causse et al., 2019; Guo et al., 2017; Wiik et al., 2015) or can be reduced or suppressed across horizon boundaries (Hoversten et al., 2015). Constraints may also be applied to favour similarities between local variations in the vertical and horizontal models (Meju et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

Causse

2023

Geophysical Prospecting

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Non‐linear inversion of controlled source electromagnetic data is non‐unique. Inversion ambiguity and uncertainty grow with model complexity and limitations in sensitivity, for example when imaging deep targets. Uncertainties should be estimated. The best way to do that is by statistical inversion techniques. Because of the large number of parameters of 3D models, these methods are too costly for 3D applications. But neglecting or underestimating uncertainties often leads to erroneous interpretation and poor exploration decisions. The number of inversion parameters can be dramatically reduced while still being able to describe a complex 3D subsurface. To obtain that, I represent prior information by an input ensemble of geologically reasonable prior models. The model space is defined by the principal directions of this ensemble, where the inversion parameters are the coefficients of the different components. In this model space, inversion becomes much more efficient and can produce an updated ensemble of posterior models at the same computational cost as a controlled‐source electromagnetic inversion project using conventional non‐linear inversion techniques. I present examples where unique Gauss–Newton inversions lead to difficult interpretations and poor conclusions about hydrocarbon presence and saturation. The new approach provides a quantitative estimation of resistivity ambiguities and uncertainties leading to better interpretation and safer decisions. The examples use 2D synthetic controlled‐source electromagnetic data and 3D controlled‐source electromagnetic field data.

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Structurally-Constrained 3D Anisotropic CSEM Resistivity Inversion with Crossgradient Criterion: Proof-Of-Concept Study

Cited by 2 publications

References 0 publications

Using Large-Size Three-Dimensional Marine Electromagnetic Data for the Efficient Combined Investigation of Natural Hydrogen and Hydrocarbon Gas Reservoirs: A Geologically Consistent and Process-Oriented Approach with Implications for Carbon Footprint Reduction

Using Large-Size Three-Dimensional Marine Electromagnetic Data for the Efficient Combined Investigation of Natural Hydrogen and Hydrocarbon Gas Reservoirs: A Geologically Consistent and Process-Oriented Approach with Implications for Carbon Footprint Reduction

Ensemble scenario‐based inversion: A new approach for estimating the uncertainty of resistivity models derived from 3D controlled source electromagnetic data

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