Ultra-Deep Azimuthal Resistivity (UDAR) measurements provide advanced subsurface imaging capabilities by bridging the gap between traditional well logging and seismic interpretation. However, interpreting UDAR measurements in complex, spatially heterogeneous, or anisotropic formations remains challenging. Traditional 2D/3D inversion procedures are computationally intensive and unsuitable for real-time applications. In this study, we develop and successfully verify a rapid, adaptive 2D/3D inversion for a robust and reliable interpretation of UDAR measurements. The Jacobian matrix is efficiently calculated and updated with a novel Block Gauss-Radau modeling algorithm via the adjoint method. The method is successfully validated with challenging synthetic and field measurements provided by various operating companies.

Ultradeep azimuthal resistivity (UDAR) measurements provide advanced subsurface imaging capabilities by bridging the gap between traditional well logging and seismic interpretation. However, interpreting UDAR measurements in complex, spatially heterogeneous, or anisotropic formations remains challenging. Traditional two-dimensional (2D) and three-dimensional (3D) inversion procedures are computationally intensive and unsuitable for real-time applications. We describe our recent progress in the development of fast modeling and inversion algorithms for the interpretation of UDAR measurements. These algorithms are designed for multi-CPU clusters and tailored for integration with local 3D geological models and arbitrary well trajectories. Modeling employs a finite-volume solution of Maxwell's equations implemented on an adaptive Lebedev grid, effectively accounting for diverse formation complexities and tool configurations. A new block-based solver computes multi-input multi-output (MIMO) responses efficiently, significantly reducing CPU runtime. Inversion is performed with a gradient-based approach (Occam-type) and incorporates regularization to account for measurement noise and non-uniqueness in the estimation. Furthermore, inversion complexity grows progressively from zero-dimensional (0D) to 3D, based on the localized dimensionality of both measurements and the spatial distribution of electrical conductivity. The method is successfully validated with challenging synthetic and field measurements acquired in the North Sea by various service companies. It is confirmed that the modeling and inversion algorithms are efficient, stable, and reliable for estimating the spatial distributions of anisotropic electrical conductivity around the well trajectory. The Jacobian matrix is efficiently calculated and updated by the modeling algorithm, which is as accurate as the traditional perturbation method but five orders of magnitude faster. In addition, we observe that both data misfit and model uncertainty decrease as inversion dimensionality increases. Our adaptive inversion method also significantly reduces computational costs by triggering expensive 2D and 3D inversions only when necessary along the well trajectory. The current modeling algorithm has the potential to be further optimized for hybrid GPU-CPU implementation to enable real-time 2D and 3D inversions.

We develop a new, efficient, and accurate method to simulate frequency-domain borehole electromagnetic (EM) measurements acquired in the presence of three-dimensional (3D) variations of the anisotropic subsurface conductivity. The method is based on solving the quasi-static Maxwell equations with a goal-oriented finite-volume discretization via block-quadrature reduced-order modeling. Discretization is performed with a Lebedev grid that enables accurate and conservative solutions in the presence of any form of anisotropic electrical conductivity. Likewise, the method makes use of a new effective-medium approximation to locally account for non-conformal boundaries and large contrasts in electrical conductivity, especially in the vicinity of EM sources and receivers. The finite-volume discretization yields a large symmetric linear system of equations, which is reduced to a set of smaller structured problems via block Lanczos recursion. The formulation also enables the efficient calculation of the adjoint solution, which is necessary for gradient-based inversion of the measurements to estimate the associated spatial distribution of electrical conductivity, i.e., to solve the inverse problem. Specific applications and verifications of the new numerical simulation algorithm are considered for the case of borehole ultra-deep azimuthal resistivity measurements (UDAR) typically used for subsurface well geosteering and navigation. We verify the efficiency, robustness, and scalability of this approach using synthetic UDAR measurements acquired in a 3D formation inspired by North-Sea geology. The numerical experiments successfully verify the applicability of our modeling approach to real-time UDAR processing frameworks.

Ultra-Deep Azimuthal Resistivity (UDAR) measurements provide advanced subsurface imaging capabilities by bridging the gap between traditional well logging and seismic interpretation. However, interpreting UDAR measurements in complex, spatially heterogeneous, or anisotropic formations remains challenging. Traditional 2D and 3D inversion procedures are computationally intensive and unsuitable for real- time applications. We describe our recent developments in fast modeling and inversion algorithms for UDAR measurements. These algorithms are designed for multi-CPU clusters and tailored for integration with local 3D geological models and arbitrary well trajectories. Modeling employs a finite-volume solution of Maxwell’s equations implemented on an adaptive Lebedev grid, effectively handling diverse formation complexities and tool configurations. A new block-based solver computes multi-input multi-output (MIMO) responses efficiently, significantly reducing runtime. Inversion is performed with a gradient-based approach (Occam-type) and incorporates regularization to account for noise and non- uniqueness. Furthermore, inversion complexity grows progressively from 0D to 3D, based on the localized dimensionality of both measurements and spatial distribution of electrical conductivity. The method is successfully validated with challenging synthetic and field measurements acquired in the North Sea by various service companies. It is confirmed that the modeling and inversion algorithms are efficient, stable, and reliable for estimating the spatial distributions of anisotropic electrical conductivity around and ahead of the well trajectory. The Jacobian matrix is efficiently calculated and updated by the modeling algorithm, which is as accurate as the traditional perturbation method but five orders of magnitude faster. In addition, we observe that both data misfit and model uncertainty decrease as inversion dimensionality increases. Our adaptive inversion method also significantly reduces computational costs by triggering the expensive 2D and 3D inversions only when necessary. The current modeling algorithm has the potential to be further optimized for hybrid GPU-CPU implementation that enables real-time 2D and 3D inversions.

Summary Carbon Capture, Utilisation, and Storage (CCUS) is a pillar of the energy transition. Two key technical elements are CO2 containment (seal integrity) and storage volumes (dynamic fluid plume) requiring geophysical monitoring. Over the past years, we carried out various electromagnetic surface monitoring surveys. When using the right workflow we can obtain surface data that is reconciled to borehole log scale. This makes the data certifiable in that context. For one application, the storage reservoir is a saline aquifer, we used Controlled Source ElectroMagnetics (CSEM) combined with magnetotellurics (MT) to obtain the background geologic structure. It included a high-power CSEM transmitter (200 KVA) and multi-component receivers to see resistive and conductive strata. Presently, we prepare for CO2 to be stored in basalt formation, where it solidifies. When CO2 is injected into basalt, it is first gaseous or liquid phase (pressure dependent) which causes a reduction in resistivity of the basalt reservoir followed by a strong resistivity increase when it solidifies. Another application is to contain CO2 under hydrocarbon reservoirs associated with salt dome provinces. We verified CSEM imaging over various salt domes to image the reservoir boundaries accurately.

Ultra-deep azimuthal resistivity (UDAR) logging technology has been around for the last two decades. However, the real-time inversion of deep-sensing borehole electromagnetic measurements is still an outstanding challenge to yield a reliable image of subsurface electrical resistivity. In this study, we develop a new procedure for adaptive 1D inversion of UDAR measurements that quantifies the uncertainty of results and implements various measures of data misfit to trigger local higher-dimensional inversions. We construct an augmented linear system for fast and stable OCCAM’s inversion that accounts for data and model weight matrices, as well as priors for adaptive inversion. We further verify the successful application of this adaptive inversion method on three resistivity models inspired by actual reservoir structures explored with commercial UDAR tool configurations.

Reliable interpretation of borehole electromagnetic (EM) measurements acquired in horizontal and high-angle wells requires fast, robust, and versatile solutions of forward and inverse problems of Maxwell’s system in complex three-dimensional (3D) anisotropic formations. Based on recent advances in numerical simulation methods, we implement 3D anisotropic EM modeling and inversion software and algorithms to simulate and quality control (QC) ultradeep azimuthal resistivity (UDAR) measurements. The combination of fast modeling and inversion under complex and anisotropic 3D earth-model conditions enables us to accurately quantify the limits of resolution and uncertainty of UDAR measurements. The software and algorithms allow fast and robust modeling based on the finite-volume homogenization technique together with a special reduced-order gridding procedure. This modeling strategy enables the use of model-independent finite-volume grids in tool coordinates combined with a global-model grid accepting inputs from commonly used 3D earth-model rendering formats. While the tool moves along the well trajectory, the formation determined on the 3D global grid shifts and rotates in tool coordinates. Furthermore, we implement several fast direct and iterative solvers in our modeling/inversion workflow, all of which yield practically identical results. Parallel computing also allows real-time modeling. Our modeling approach is effective for the multidimensional inversion of UDAR profiling/logging along arbitrary well trajectories. Benchmarks and examples of UDAR simulations on operator’s 3D subsurface models confirm the efficacy of our simulation method. The accompanying figure describes a benchmark example including a 3D simulation of commercial UDAR measurements acquired across a spatially complex formation model with two faults. Numerical simulation time for 3,000 couplings of logging points and tool configurations is less than 8 CPU hours on a typical laptop and less than 20 seconds on a supercomputer. The benchmark was also verified against an independent 3D EM modeling method. Our 3D fully anisotropic modeling software can be used for real-time inversion QC of commercial UDAR tool measurements. A 3D simulation based on a two-dimensional (2D) model of the well curtain section (obtained as stitched-together 1D models: results obtained from 1D inversion of commercial measurements) and comparison of this simulation to actual tool measurements identify the sections of the well trajectory where 2D-3D inversion is needed to decrease the data misfit to acceptable values (i.e., measurement noise levels). Future endeavors include fast, fully anisotropic 2D-3D measurement simulation using adaptive upscaling of 3D models and novel 2D-3D inversion algorithms specifically designed for UDAR measurement conditions. Our goal is to develop real-time 2D and 3D inversion of UDAR measurements for well geosteering and refined 3D subsurface model rendering as additional measurements and geometrical constraints are included into the inversion by asset teams.

The Controlled-Source ElectroMagnetic (CSEM) method provides crucial information about reservoir fluids and their spatial distribution. Carbon dioxide (CO2) storage, enhanced oil recovery (EOR), geothermal exploration, and lithium exploration are ideal applications for the CSEM method. The versatility of CSEM permits its customization to specific reservoir objectives by selecting the appropriate components of a multi-component system. To effectively tailor the CSEM approach, it is essential to determine whether the primary target reservoir is resistive or conductive. This task is relatively straightforward in CO2 monitoring, where the injected fluid is resistive. However, for scenarios involving brine-saturated (water-wet) or oil-wet (carbon capture, utilization, and storage—CCUS) reservoirs, consideration must also be given to conductive reservoir components. The optimization of data acquisition before the survey involves analyzing target parameters and the sensitivity of multi-component CSEM. This optimization process typically includes on-site noise measurements and 3D anisotropic modeling. Based on our experience, subsequent surveys tend to proceed smoothly, yielding robust measurements that align with scientific objectives. Other critical aspects to be considered are using magnetotelluric (MT) measurements to define the overall background resistivities and integrating real-time quality assurance during data acquisition with 3D modeling. This integration allows the fine tuning of acquisition parameters such as acquisition time and necessary repeats. As a result, data can be examined in real-time to assess subsurface information content while the acquisition is ongoing. Consequently, high-quality data sets are usually obtained for subsequent processing and initial interpretation with minimal user intervention. The implementation of sensitivity analysis during the inversion process plays a pivotal role in ensuring that the acquired data accurately respond to the target reservoirs’ expected depth range. To elucidate these concepts, we present an illustrative example from a CO2 storage site in North Dakota, USA, wherein the long-offset transient electromagnetic method (LOTEM), a variation of the CSEM method, and the MT method were utilized. This example showcases how surface measurements attain appropriately upscaled log-scale sensitivity. Furthermore, the sensitivity of the CSEM and MT methods was examined in other case histories, where the target reservoirs exhibited conductive properties, such as those encountered in enhanced oil recovery (EOR), geothermal, and lithium exploration applications. The same equipment specifications were utilized for CSEM and MT surveys across all case studies.

Electromagnetic measurements can significantly contribute to imaging and monitoring of the reservoir fluid movement for hydrocarbon, geothermal, and CO2 sequestration scenarios. Among electromagnetic measurements, passive methods such as magnetotellurics (MT) give a good overview picture while controlled source electromagnetics (CSEM) addresses more details of the flood front location. Since Earth’s resistivities vary over several orders of magnitude, adopting the methodology to the target resistivity is important. For conductive targets, we usually use magnetic fields and for resistive targets the electric fields (but not exclusively). Using field data from hydrocarbon and CO2 applications, we illustrate the importance of a workflow and adaption to the target on hand. Verifying the geophysical acquisition and processing steps with 3D modeling and checking them against a 3D anisotropic log-derived model maintains confidence in the workflow and minimizes the influence on the data. This allows us to predict data validity and to certify the data with respect to the borehole logs.

Focusing geophysics to improve recovery factor of hydrocarbon reservoirs adds value and contributes toward ZERO carbon footprint by increasing the recovery factor by of 30-40 % and thus reducing the cost/carbon emission per produced barrel. Thus, the Enhanced Oil Recovery (EOR) market is expected to grow more than 3.5% annually. This will be even more fueled by the Green-House-Gas (GHG) reduction and subsequent CO2 injection into the reservoirs as they are being produced. Presently, geophysics only ac-counts for a small percentage of this market, thus its growth is inevitable since more deterministic observation lead higher operating efficiency. Imaging the fluids (hydro-carbon, water, and CO2) is a key component to optimized production and injection. We designed a novel electromagnetic (EM) acquisition system that combines mul-ti-physics fluid imaging and acquires surface and borehole data with high fidelity. Borehole calibration is needed to upscale reservoir data and parameters to measurement scale. Multiple electromagnetic methods are used as well as microseismics in one layout for Exploration and Production (E & P) problems. Multi-components in electromagnetics allows resolving oil and water-bearing zones equally well while achieving the best ac-curacy suitable for repeat measurements. Because sedimentary basins are intrinsically anisotropic, considering 3-dimensional anisotropy is essential from measurement and 3D modeling viewpoint. Thus, the results have the better subsurface images. Here, we combine hardware design, methodology, 3D modeling, processing, and interpretations into an integrated technology and demonstrate the success with verifiable case histories.

The North Dakota CarbonSAFE (Carbon Storage Assurance Facility Enterprise) project is part of the U.S. Department of Energy initiative to develop geologic storage sites to store 50+ million metric tons of carbon dioxide (CO2) from industrial sources. Geophysical methods are key for characterizing the geologic formations to store CO2 and monitor the injected CO2 over time to ensure containment. In the integrated multimeasurement geophysical approach considered for this project, it is expected that the controlledsource electromagnetic (CSEM) method is a strong contributor to mapping the CO2 movement. A feasibility study of the CSEM method, including 1D and 3D modeling and a field noise test, was conducted to determine its effectiveness in monitoring CO2 in the Broom Creek and Deadwood Formations. The study results demonstrate that the CSEM method can be used for CO2 storage monitoring in the study area. Preliminary inversion results of magnetotelluric (MT) and CSEM field data confirm the quality of the anisotropic model developed in this study.

Fluid imaging is one of the key geophysical technologies for the energy industry during energy transition to zero footprint. We propose better Cloud-based fluid distribution imaging to allow better, more optimized production, thus reducing carbon dioxide (CO2) footprint per barrel produced. For CO2 storage, the location knowledge of the stored fluids is mandatory. Electromagnetics is the preferred way to image reservoir fluids due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to cost optimization of field operations. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and even, for the interpretation, to close to 24 h. This then opens new doors for the breakthrough of this technology from exploration to production and monitoring. It allows the application envelope to be enlarged to much noisier environments where real time acquisition can be optimized based on the acquired data. Once all components are commercialized, the full implementation could become a real game changer by providing near real time 3-dimensional subsurface images in support of the energy transition.

Exploration for hydrocarbon is often difficult when the overlaying strata is of high seismic velocity as it is for basalt, salt and carbonates. Many the world reservoirs are in carbonates. Mapping the reservoir laterally is thus difficult and developing carbonate reservoirs is expensive. We propose to use controlled source electromagnetic (CSEM) method to image the fluid better. In SE Asia commonly magnetotellurics, a passive method, is being used [1], but they are less sensitive to deep subsurface resistivity variations as CSEM. Pioneering work with CSEM was done in the 1980s in Australia and Europe [2] where CSEM was used to map resistive reservoirs / carbonates. Since then, the equipment and modeling methods have significantly improved, and the problem is addressed more cost effective thus reducing exploration cost by several fold. Applying a new differential measurement methodology and using 3-dimensioanal (3D) anisotropic model derived from well logs and 3D modeling we are able remove near surface anomalies and illuminate the deep the reservoir target and its lateral variations. Thus, we have a cost-effective solution for many exploration and production problems associated with carbonates.

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Quality control of ultra-deep resistivity imaging using fast 3D electromagnetic modelingAuthors: Sofia DavydychevaVladimir DruskinLeonid KnizhnermanMichael RabinovichSofia Davydycheva3DEM HoldingSearch for more papers by this author, Vladimir DruskinWorcester Polytechnic InstituteSearch for more papers by this author, Leonid Knizhnerman3DEM HoldingSearch for more papers by this author, and Michael RabinovichBPSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3425809.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFull 3D electromagnetic modeling based on optimal multiscale Lebedev’s discretization of Maxwell’s equations with general anisotropy is used for reliable modeling and quality control of ultra-deep electromagnetic (EM) measurements. With this fast and robust full 3D anisotropic modeling software we calculate the tool responses in provided 2D/3D models and compare them to the respective 1D modeling results or to the real data whenever available. The observed differences are analyzed for the resistivity and for the directional curves. We often observe significant differences between 3D and local 1D responses approximating real data since the ultra-deep tool length is typically comparable with size of detected 3D anomalies and cannot be neglected especially when transmitters and receivers are in different structures. Using the fast and accurate 3D modeling we also study various effects as 3D features, anisotropy, changing dips, etc. The accurate full 3D modeling with general anisotropy is critically important for quality control of the standard deep resistivity image and distance-to-bed answer products based on the local 1D inversion. We illustrate our modeling and QC approach on the ultra-deep resistivity data acquired in a horizontal well in the North Sea. 3D modeling & 2D/3D inversion can significantly improve answer products for the ultra-deep resistivity tools.Tuesday, October 13, 2020Session Start Time: 1:50 PMPresentation Time: 2:40 PMLocation: 351DPresentation Type: OralKeywords: 3D, electromagnetics, finite difference, borehole measurements, logging while drillingPermalink: https://doi.org/10.1190/segam2020-3425809.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Sofia Davydycheva, Vladimir Druskin, Leonid Knizhnerman, and Michael Rabinovich, (2020), "Quality control of ultra-deep resistivity imaging using fast 3D electromagnetic modeling," SEG Technical Program Expanded Abstracts : 380-384. https://doi.org/10.1190/segam2020-3425809.1 Plain-Language Summary Keywords3Delectromagneticsfinite differenceborehole measurementslogging while drillingPDF DownloadLoading ...

Electromagnetic (EM) method, especially Controlled Source Electromagnetic (CSEM) are a unique way of observing fluid movement at depth from the surface. The depth range of interest for hydrocarbon and geothermal applications is from about 500m to 5-7 km. For hydrocarbon reservoirs monitoring we focus to improve the recovery factors and understand fluid movement resulting in lower carbon footprint per produced barrel of oil. For geothermal applications, we can observe magma movements as aid for volcano eruption prediction or monitor producing geothermal field to improve production efficiency and to observe (in correlation with microseismic) reservoir damage and potential induced seismicity.

24 EM Induction Workshop, Helsingør, Denmark, August 12-19, 2018 1 / 4 1D and 3D EM interpretations to confirm the salt dome profile A.Y. Paembonan, R. Arjwech, Sofia Davydycheva, T. Hanstein, M. Smirnov, K. M. Strack 1 Department of Geotechnology, Khon Kaen University, Thailand, Te@kku.ac.th 2 Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden, maxim.smirnov@ltu.se 3 KMS Technologies, USA, info@KMSTechnologies.com

24 EM Induction Workshop, Helsingør, Denmark, August 12-19, 2018 1 / 4 EM-microseismic reservoir monitoring system Sofia Davydycheva, T. Hanstein, M. Smirnov, K. M. Strack 1 KMS Technologies, USA, info@KMSTechnologies.com 2 Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Sweden, maxim.smirnov@ltu.se

A new technology for reservoir monitoring includes full field controlled-source electromagnetics (CSEM) and microseismics. To mitigate the risk, we have developed full technology cycle: from patents, hardware, acquisition methodology, to data processing and interpreting in 3D. The system can acquire surface-to-surface and surface-to-borehole measurements. EM data are used to track fluids, due to their high sensitivity to the fluid resistivity while seismic data relate primarily to the reservoir boundaries. Having seismic and EM sensors in the same recording unit allows the addressing of the multi-physics character of the problem early on in the workflow. The system enables acquiring large number of EM data channels at low cost similar to what is done with seismic data. Typical risks are lack of EM image focus, formation resistivity anisotropy and unaccounted effects of steel casing(s). To mitigate these risks, we apply careful 3D modeling feasibility studies and acquire dense EM field data. In addition, we apply a novel method to focus the EM image information directly below the receiver. Operational risks include low signal-to-noise (SNR) ratio, issues related to the transmitter stability, to the stability of the groundings, and data processing inefficiency. Understanding and mitigation of these risks is key to successful reservoir monitoring job. Presentation Date: Tuesday, October 16, 2018 Start Time: 9:20:00 AM Location: Poster Station 15 Presentation Type: Poster

Over the last 6 years we developed an array system for electromagnetic acquisition (magnetotelluric & long offset transient electromagnetics [LOTEM]) that includes microseismic acquisition. While predominantly used for magnetotellurics, we focus on the autonomous operation as reservoir monitoring system including a shallow borehole receiver and 100/150 KVA transmitter. A marine extension is also under development. For Enhanced Oil recovery (EOR), in addition to reservoir flood front movements, reservoir seal integrity has become an issue [1]. Seal integrity is best addressed with microseismics while the water flood front is best addressed with electromagnetics. Since the flooded reservoir is conductive and the hydrocarbon saturated part is resistive, you need both magnetic and electric fields. The fluid imaging is addressed using electromagnetics. To overcome the volume-focus inherent to electromagnetics a new methodology to focus the sensitivity under the receiver is proposed. Field data and 3D modeling confirm this could increase the efficiency of LOTEM to reservoir monitoring.