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Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS

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A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 15918

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Dr. Paulo T. L. Menezes

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1. Departamento de Geologia Aplicada da Faculdade de Geologia, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20943-000, Brazil
2. Petrobras, Rio de Janeiro 20031-912, Brazil
Interests: applied geophysics; petroleum exploration; exploration geophysics
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Special Issue Information

Dear Colleagues,

Since oil and gas exploration began, applied geophysics methods have developed a fundamental role in industry, permeating applications in all life stages of hydrocarbon reservoirs.

Over this period, technological advances in many areas of applied geophysics have allowed for better subsurface imaging and characterization of reservoirs, producing positive impacts in terms of reduced exploratory costs and especially in the optimization of exploration, aiming at a lower environmental impact in energy production and a more sustainable hydrocarbon industry. Notably, carbon capture, utilization and storage (CCUS) projects play an essential role in decarbonization efforts as countries worldwide aim to reduce emissions from the energy industry. This, together with energy storage in deep reservoirs (hydrogen, compressed air, etc.) are the key vectors of today’s environmental and energy policies.

Thus, a platform to trade and debate this new knowledge for the future of hydrocarbon exploration, CCUS applications and energy storage (including geothermal) is essentially required, which is the aim of this Special Issue.

Our focus in this Special Issue includes geophysical method applications from regional exploration to reservoir characterization and monitoring as well as carbon and energy storage solutions. We especially welcome the submission of case studies, reviews, new developments, and the integration of methodologies. We have divided the themes into three sections.

Section 1: Exploration case studies, from regional to local scales.

Section 2: Reservoir characterization and monitoring.

Section 3. Applied geophysics in carbon capture, utilization and storage (CCUS) and energy storage.

We look forward to your contributions to this Special Issue.

Dr. Paulo T. L. Menezes
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

hydrocarbon exploration: greenfields and brownfields
advances in integrated interpretation and multi-physics methods
reservoir characterization and monitoring
energy super basins characterization and risk assessment
carbon capture, utilization and storage (CCUS)

Published Papers (11 papers)

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Editorial

Jump to: Research, Review

4 pages, 172 KiB

Open AccessEditorial

Editorial for the Special Issue “Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS”

by Paulo T. L. Menezes

Minerals 2023, 13(10), 1335; https://doi.org/10.3390/min13101335 - 17 Oct 2023

Viewed by 881

Abstract

Since its inception, applied geophysics methods have been crucial in the oil and gas exploration industry [...] Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

Research

Jump to: Editorial, Review

37 pages, 15998 KiB

Open AccessFeature PaperArticle

Application of Electromagnetic Methods for Reservoir Monitoring with Emphasis on Carbon Capture, Utilization, and Storage

by César Barajas-Olalde, Donald C. Adams, Ana Curcio, Sofia Davydycheva, Ryan J. Klapperich, Yardenia Martinez, Andri Y. Paembonan, Wesley D. Peck, Kurt Strack and Pantelis Soupios

Minerals 2023, 13(10), 1308; https://doi.org/10.3390/min13101308 - 10 Oct 2023

Cited by 3 | Viewed by 1561

Abstract

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. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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26 pages, 26513 KiB

Open AccessArticle

Prediction of Reflection Seismic Low-Frequency Components of Acoustic Impedance Using Deep Learning

by Lian Jiang, John P. Castagna, Zhao Zhang and Brian Russell

Minerals 2023, 13(9), 1187; https://doi.org/10.3390/min13091187 - 10 Sep 2023

Cited by 2 | Viewed by 969

Abstract

The unreliable prediction of the low-frequency components from inverted acoustic impedance causes uncertainty in quantitative seismic interpretation. To address this issue, we first calculate various seismic and geological attributes that contain low-frequency information, such as relative geological age, interval velocity, and integrated instantaneous amplitude. Then, we develop a method to predict the low-frequency content of seismic data using these attributes, their high-frequency components, and recurrent neural networks. Next, we test how to predict the low-frequency components using stacking velocity obtained from velocity analysis. Using all the attributes and seismic data, we propose a supervised deep learning method to predict the low-frequency components of the inverted acoustic impedance. The results obtained in both synthetic and real data cases show that the proposed method can improve the prediction accuracy of the low-frequency components of the inverted acoustic impedance, with the best improvement in a real data example of 57.7% compared with the impedance predicted using well-log interpolation. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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17 pages, 11968 KiB

Open AccessArticle

Source Rock Evaluation from Rock to Seismic Data: An Integrated Machine-Learning-Based Work Flow and Application in the Brazilian Presalt (Santos Basin)

by Maria Anna Abreu de Almeida dos Reis, Andrea Carvalho Damasceno, Carlos Eduardo Dias Roriz, André Leonardo Korenchendler, Atilas Meneses da Silva, Eric da Silva Praxedes and Vitor Gorni Silva

Minerals 2023, 13(9), 1179; https://doi.org/10.3390/min13091179 - 08 Sep 2023

Cited by 1 | Viewed by 1225

Abstract

The capacity to predict the occurrence and quality of source rocks in a sedimentary basin is of great economic importance in the evaluation of conventional and non-conventional petroleum resources. Direct laboratory examinations of rock samples are the most accurate way to obtain their geochemical properties. However, rock information is usually sparse, and source rocks are often sampled at positions that may not be representative of the average organic content and quality of oil kitchens. This work proposes a work flow supported by machine learning methods (random forest, DBSCAN, and NGBoost) to automate the source rock characterization process to maximize the use of available data, expand data information, and reduce data analysis time. From the automated quality control of the input data through the extrapolation of laboratory measurements to continuous well logs of geochemical properties, culminating in the 3D estimation of these properties, we generate volumes of total organic carbon (TOC) by applying machine learning techniques. The proposed method provides more accurate predictions, reducing uncertainties in the characterization of source rocks and assisting in exploratory decision making. This methodology was applied in the presalt source rocks from Santos Basin (Brazil) and allowed us to quantify the TOC distribution, improving the interpretation of the main source rock interval top and base based only on seismic amplitude data. The result suggests higher TOC values in the northern and western grabens of the studied area and a higher charge risk in the eastern area. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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20 pages, 5824 KiB

Open AccessFeature PaperArticle

Basement Mapping Using Nonlinear Gravity Inversion with Borehole and Seismic Constraints

by Julio Cesar S. O. Lyrio and Yaoguo Li

Minerals 2023, 13(9), 1173; https://doi.org/10.3390/min13091173 - 06 Sep 2023

Cited by 1 | Viewed by 658

Abstract

We present an integrated method for mapping the basement structures of sedimentary basins by combining surface gravity data, seismic imaging, and borehole logging information. The core of the method is a nonlinear inversion algorithm for constructing the shape and depth of the basement from surface gravity data. By using the primal-logarithmic barrier method, we impose depth constraints from the borehole information. The basement depth was imaged by seismic interpretation and incorporated into the inversion as a reference model. As a result, the gravity inversion constructs basement structures that are closest to the seismic input while simultaneously satisfying the surface gravity data and borehole information. We used this new methodology to unveil the basement morphology of the Recôncavo Basin, Brazil. Recôncavo is a syn-rift onshore mature basin that exhibits a strong correlation between oil field distribution and tectonic framework. The seismic imaging in the area is ambiguous, and our approach improved the basement definition and highlighted exploration targets in the studied area. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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17 pages, 3083 KiB

Open AccessArticle

Feasibility of 4D Gravity Monitoring in Deep-Water Turbidites Reservoirs

by Andre D. Arelaro, Valeria C. F. Barbosa, Vanderlei C. Oliveira Jr and Paulo T. L. Menezes

Minerals 2023, 13(7), 907; https://doi.org/10.3390/min13070907 - 05 Jul 2023

Cited by 1 | Viewed by 985

Abstract

We present a seafloor 4D gravity feasibility analysis for monitoring deep-water hydrocarbon reservoirs. To perform the study, we have simulated the gravity effect due to different density and pore pressure distributions derived from a realistic model of a turbiditic oil field in Campos Basin, offshore Brazil. These reservoirs are analogs of several other passive-margin turbiditic systems located around the world. We considered four reservoir scenarios including and not including seafloor subsidence. Our results indicate that the gravity responses are higher than the feasible value of 3 μGal 12 years following the base survey. The area of maximum gravity anomaly corresponds to where we suppose hydrocarbon extraction occurs. A maximum seafloor subsidence of 0.6 cm was estimated, resulting in no detectable gravity effects. Our results endorse the 4D seafloor gravity acquisition as a beneficial tool for monitoring deep-water passive-margin turbiditic reservoirs. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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25 pages, 13322 KiB

Open AccessFeature PaperArticle

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

by Max A. Meju and Ahmad Shahir Saleh

Minerals 2023, 13(6), 745; https://doi.org/10.3390/min13060745 - 30 May 2023

Cited by 3 | Viewed by 2001

Abstract

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. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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19 pages, 6586 KiB

Open AccessArticle

Geophysical Characterization and Attenuation Correction Applied for Hydrate Bearing Sediments in Japan Sea

by Luiz Alberto Santos, Ryo Matsumoto, Fernanda Darcle Silva Freitas and Marco Antonio Cetale Santos

Minerals 2023, 13(5), 655; https://doi.org/10.3390/min13050655 - 10 May 2023

Cited by 2 | Viewed by 1217

Abstract

Estimation of rock properties from seismic data is important for exploration and production activities in the petroleum industry. Considering the compressional velocity—the speed of propagating body waves in formations—and the quality factor (Q)—a measure of the frequency-selective energy losses of waves propagating through formations—both properties are usually estimated from multichannel seismic data. Velocity is estimated during multichannel processing of seismic reflection data in either the time or depth domain. In marine seismic acquisition, Q can be estimated from the following sources: Vertical Seismic Profile (VSP) surveys, where sources are located near the sea surface and geophones are distributed at depth along a borehole; and multichannel reflection data, where sources are also located near the sea surface and receivers are distributed either at the sea surface (conventional seismic survey with streamers) or on the sea floor (use of nodes or Ocean Bottom Cables (OBC)). The aforementioned acquisition devices, VSP, conventional streamers, nodes, and OBCs are much more expensive than single-channel acquisition with one receiver per shot due to the cost of operation. There are numerous old and new datasets from academia and the oil industry that have been acquired with single-channel acquisition devices. However, there is a paucity of work addressing the estimation of velocity and Q from this type of equipment. We investigate the estimation of Q and velocity from single-channel seismic data. Using the windowed discrete Fourier transform for a single seismic trace, we calculate the peak and dominant frequency that changes with time. In the geologic environment, higher frequencies are attenuated at shallow depths (time), while lower frequencies remain at deeper positions. From the rate at which higher frequencies are attenuated with time, we estimate the effective quality factor (Q_eff). However, when using Kirchhoff migration to process single-channel seismic data, events far from the vertical projection of the receiver contribute to the trace at a given time. Then, an underestimation of the effective quality factor occurs. To compensate for the effects of more distant events with lower-frequency content contaminating the shorter events, we propose a linear equation to correct the effective quality factor estimated from migrated seismic data. Effective Q and its correction are estimated in five single-channel seismic lines surveyed along the Joetsu Knoll, a SW-NE anticline structure on the eastern margin of the Sea of Japan. These results are linked to geomorphological and geological features and the velocity field. Joetsu Knoll is a known site of massive gas hydrates (GH), which occur in the first hundred metres of Neogene sediments and, together with gas chimneys, play an important role in seismic wave absorption. Q_eff estimated from migrated seismic data maintains the spatial relationship between high and low Q regions. The region of low Q, which is below 124 and has an average value of 57, occurs near the anticlinal hinge and tends to coincide with the region in which the Bottom Simulating Reflector (BSR) resides. The coexistence of GH and free gas coincides with the very low P velocity gradient of 0.225 s⁻¹. BSR occurrence, Q_eff and the geometry of the Joetsu anticline testify to progressive gas hydrate depletion northward along the dome. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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12 pages, 30831 KiB

Open AccessFeature PaperArticle

New Concept Ocean-Bottom Multiphysics (OBMP) Nodes for Reservoir Monitoring

by Paulo T. L. Menezes, Andrea Zerilli, Jorlivan L. Correa, Everton N. Menor, Sergio M. Ferreira and Tiziano Labruzzo

Minerals 2023, 13(5), 602; https://doi.org/10.3390/min13050602 - 27 Apr 2023

Cited by 2 | Viewed by 1663

Abstract

Marine-controlled source electromagnetics (CSEM) have been extensively applied to various exploration scenarios worldwide. However, its perceived value and cost relative to seismic and the scarcity of realistic case studies have limited the industry’s interest in time-lapse reservoir-monitoring (4D) applications. A feasible way to make demand for CSEM for 4D-monitoring programs would be to increase the value of information and reduce survey costs by performing joint operations where seismic and CSEM data are acquired during the same survey and at equivalent spatial densities. To this end, we propose a new multiphysics ocean-bottom nodes (OBN) concept and show the industry that CSEM can be a cost efficient and effective integrators to 4D seismic projects. To this end, we conducted a feasibility study demonstrating that horizontal magnetic field components have the required sensitivities and can be used instead of horizontal electric field components in mapping the 3D resistivity distribution and 4D fluid change responses in a given reservoir. This makes engineering a new OBN class simpler and cheaper, as various miniaturized magnetic field sensors are available off-the-shelf or readily working along with packaging and coupling solutions. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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20 pages, 3873 KiB

Open AccessArticle

An Integrated Deep Learning Framework for Classification of Mineral Thin Sections and Other Geo-Data, a Tutorial

by Paolo Dell’Aversana

Minerals 2023, 13(5), 584; https://doi.org/10.3390/min13050584 - 22 Apr 2023

Cited by 2 | Viewed by 1961

Abstract

Recent studies have demonstrated the potential of machine learning methods for fast and accurate mineral classification based on microscope thin sections. Such methods can be extremely useful to support geoscientists during the phases of operational geology, especially when mineralogical and petrological data are fully integrated with other geological and geophysical information. In order to be effective, these methods require robust machine learning models trained on pre-labeled data. Furthermore, it is mandatory to optimize the hyper-parameters of the machine learning techniques in order to guarantee optimal classification accuracy and reliability. Nowadays, deep learning algorithms are widely applied for image analysis and automatic classification in a large range of Earth disciplines, including mineralogy, petrography, paleontology, well-log analysis, geophysical imaging, and so forth. The main reason for the recognized effectiveness of deep learning algorithms for image analysis is that they are able to quickly learn complex representations of images and patterns within them. Differently from traditional image-processing techniques based on handcrafted features, deep learning models automatically learn and extract features from the data, capturing, in almost real-time, complex relationships and patterns that are difficult to manually define. Many different types of deep learning models can be used for image analysis and classification, including fully connected deep neural networks (FCNNs), convolutional neural networks (CNNs or ConvNet), and residual networks (ResNets). In this paper, we compare some of these techniques and verify their effectiveness on the same dataset of mineralogical thin sections. We show that the different deep learning methods are all effective techniques in recognizing and classifying mineral images directly in the field, with ResNets outperforming the other techniques in terms of accuracy and precision. In addition, we compare the performance of deep learning techniques with different machine learning algorithms, including random forest, naive Bayes, adaptive boosting, support vector machine, and decision tree. Using quantitative performance indexes as well as confusion matrixes, we demonstrate that deep neural networks show generally better classification performances than the other approaches. Furthermore, we briefly discuss how to expand the same workflow to other types of images and geo-data, showing how this deep learning approach can be generalized to a multiscale/multipurpose methodology addressed to the analysis and automatic classification of multidisciplinary information. This article has tutorial purposes, too. For that reason, we will explain, with a didactical level of detail, all the key steps of the workflow. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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Review

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15 pages, 18401 KiB

Open AccessFeature PaperReview

Twenty Years of CSEM Exploration in the Brazilian Continental Margin

by Paulo T. L. Menezes, Sergio M. Ferreira, Jorlivan L. Correa and Everton N. Menor

Minerals 2023, 13(7), 870; https://doi.org/10.3390/min13070870 - 28 Jun 2023

Cited by 2 | Viewed by 1414

Abstract

The controlled source electromagnetic (CSEM) method is frequently used as a risk reduction tool in hydrocarbon exploration. This paper aims to provide a comprehensive historical review of the CSEM method’s twenty-year history in the Brazilian continental margin. Since 2003, we have significantly improved our understanding of CSEM resistivity data across various geological scenarios. This review presents a roadmap of the technical advancements in acquisition design and interpretation techniques. As a result, our understanding of the methodology has broadened from traditional to more general use, such as salt imaging, gas hydrates, geohazard mapping, and reservoir characterization. Finally, we indicate the potential upcoming CSEM applications in new energy resources and carbon capture and storage. Full article

(This article belongs to the Special Issue Applied Geophysics in Hydrocarbon Exploration, Energy Storage and CCUS)

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