TECHNICAL PROGRAMME | Primary Energy Supply – Future Pathways
Advances in Geoscience
Forum 5 | Hall 10 - SABIC Technical Programme 1
14
October
14:30
15:45
UTC+3
Advances in geoscience are pivotal in revolutionising energy supply, improving resource management, and addressing environmental challenges. By integrating cutting-edge technologies and innovative methods, geoscientists are paving the way for a more sustainable and efficient future in energy production and resource utilisation.
The oil and gas industry is undergoing a significant transformation fueled by the rapid advancements of digital technologies. The integration of advanced digitalization tools and artificial intelligence (AI) methodologies is revolutionizing the field of geoscience, supporting optimization of complex geological workflows, unlocking new insights from diverse data sources, and reactivating legacy assets. This digital transformation deepens the understanding of subsurface reservoirs and empowers more informed, efficient, and effective decision-making. By leveraging multi-scale, multimodal digital imaging and AI methods, we can analyze vast datasets and extract valuable insights from millions of geological rock samples and analytical reports. This paper highlights the potential of digital transformation to revolutionize oil and gas geoscience, with the goal of transforming exploration workflows and reservoir characterization outcomes.
The discovery of oil in Saudi Arabia in the 1930s marked the beginning of a rich legacy of geological knowledge, resulting in a vast and diverse repository of operational data and geological sample sets. However, the unstructured and variable nature of this legacy data has hindered its full potential, limiting its accessibility and integration. To overcome this challenge, we employed Generative AI (GenAI) technologies to transform the original data into a structured database, utilizing Optical Character Recognition (OCR) models and Retrieval-Augmented Generation (RAG) pipelines. This approach enabled efficient access and utilization of the information, including the application of AI-driven analytics. Concurrently, we digitized and analyzed a vast collection of subsurface rock samples to create a 'Digital Rock,' generating high-fidelity digital twins of the rock samples and predicting rock properties via simulations of physical and chemical processes at the pore scale.
Our digital transformation initiatives have significantly enhanced the efficiency of exploration and petroleum system analysis, saving thousands of man-hours in the search and retrieval of geological information. The Digital Rock has unlocked novel insights into complex petroleum system elements, including microporous, bioturbated, thin-bedded, tight sandstones, hot shales, and other challenging formations. Two exemplary case studies demonstrate the potential of digital transformation: (1) the regularization of over 90 years of biostratigraphic analysis datasets using GenAI, and (2) the assessment of flow heterogeneity in mega-porous reservoirs utilizing a multi-scale digital imaging workflow. These applications underscore the transformative potential of digital technologies in geoscience, enabling more accurate and efficient reservoir characterization.
The integration of rock sample images with corresponding interpretation reports has enabled the creation of a comprehensive and robust database, providing a solid foundation for training and validating AI models. This multidisciplinary approach has significantly enhanced our understanding of the subsurface environment. The synergistic relationship between geological data, interpretation, and AI-modeling has the potential to revolutionize the field of geoscience, enabling more informed decision-making for effective exploration and field development strategies.
The discovery of oil in Saudi Arabia in the 1930s marked the beginning of a rich legacy of geological knowledge, resulting in a vast and diverse repository of operational data and geological sample sets. However, the unstructured and variable nature of this legacy data has hindered its full potential, limiting its accessibility and integration. To overcome this challenge, we employed Generative AI (GenAI) technologies to transform the original data into a structured database, utilizing Optical Character Recognition (OCR) models and Retrieval-Augmented Generation (RAG) pipelines. This approach enabled efficient access and utilization of the information, including the application of AI-driven analytics. Concurrently, we digitized and analyzed a vast collection of subsurface rock samples to create a 'Digital Rock,' generating high-fidelity digital twins of the rock samples and predicting rock properties via simulations of physical and chemical processes at the pore scale.
Our digital transformation initiatives have significantly enhanced the efficiency of exploration and petroleum system analysis, saving thousands of man-hours in the search and retrieval of geological information. The Digital Rock has unlocked novel insights into complex petroleum system elements, including microporous, bioturbated, thin-bedded, tight sandstones, hot shales, and other challenging formations. Two exemplary case studies demonstrate the potential of digital transformation: (1) the regularization of over 90 years of biostratigraphic analysis datasets using GenAI, and (2) the assessment of flow heterogeneity in mega-porous reservoirs utilizing a multi-scale digital imaging workflow. These applications underscore the transformative potential of digital technologies in geoscience, enabling more accurate and efficient reservoir characterization.
The integration of rock sample images with corresponding interpretation reports has enabled the creation of a comprehensive and robust database, providing a solid foundation for training and validating AI models. This multidisciplinary approach has significantly enhanced our understanding of the subsurface environment. The synergistic relationship between geological data, interpretation, and AI-modeling has the potential to revolutionize the field of geoscience, enabling more informed decision-making for effective exploration and field development strategies.
Core data has traditionally served as the foundational reference for formation evaluation by providing direct measurements of reservoir petrophysical properties. Following core retrieval, plugs intended for special core analysis (SCAL) are preserved, while routine analysis plugs must undergo extensive cleaning to remove hydrocarbons and salts, typically through Soxhlet extraction using hot toluene and methanol. This cleaning process can extend over several weeks, depending on factors such as rock permeability, pore structure complexity, and the type of hydrocarbons present. In this context, emerging digital imaging technologies, such as whole-core computed tomography (CT) scanning, have become particularly valuable by offering a rapid, nondestructive method for detailed core characterization and representative sample selection.
Whole-core CT imaging is a widely adopted technique in the oil and gas industry for assessing the internal structure of core samples. It enables quick visualization of core tubes for sample selection and provides critical geological and petrophysical insights, including the identification of fractures, facies transitions, and porosity variations. A major advantage of X-ray CT imaging lies in its ability to generate continuous, high-resolution images coupled with quantitative data essential for core evaluation. In this study, standard whole-core CT scanning was conducted at a single high-energy setting (140 kV) to primarily capture density variations along the core length, and the extracted data were utilized for porosity prediction. These continuous datasets are particularly valuable during the early phases of core analysis programs for characterizing reservoir heterogeneity and optimizing SCAL sample selection.
The primary objective of this study is to accelerate porosity estimation by integrating whole-core CT scanning, 3D virtual core plug acquisition, and quantitative CT data analysis, while also examining CT responses across different Winland r35 rock types.
The 3D virtual core plugging technique, a relatively recent innovation, involves creating digital replicas of core plugs of any desired diameter using specialized software. In this study, approximately seventy virtual plug samples were acquired from 30-meter-long sandstone and carbonate cores. Virtual plug porosity was calculated through correlations established between bulk density and CT numbers and compared with laboratory-measured helium porosity. A strong correlation was observed between the CT-derived and helium porosity values, as evidenced by a high coefficient of determination (R²).
This integrated approach not only streamlines early-stage porosity estimation but also significantly enhances SCAL sampling strategies, offering particular advantages in thinly laminated, fractured, and heterogeneous cores where physical plug acquisition can be operationally challenging.
Whole-core CT imaging is a widely adopted technique in the oil and gas industry for assessing the internal structure of core samples. It enables quick visualization of core tubes for sample selection and provides critical geological and petrophysical insights, including the identification of fractures, facies transitions, and porosity variations. A major advantage of X-ray CT imaging lies in its ability to generate continuous, high-resolution images coupled with quantitative data essential for core evaluation. In this study, standard whole-core CT scanning was conducted at a single high-energy setting (140 kV) to primarily capture density variations along the core length, and the extracted data were utilized for porosity prediction. These continuous datasets are particularly valuable during the early phases of core analysis programs for characterizing reservoir heterogeneity and optimizing SCAL sample selection.
The primary objective of this study is to accelerate porosity estimation by integrating whole-core CT scanning, 3D virtual core plug acquisition, and quantitative CT data analysis, while also examining CT responses across different Winland r35 rock types.
The 3D virtual core plugging technique, a relatively recent innovation, involves creating digital replicas of core plugs of any desired diameter using specialized software. In this study, approximately seventy virtual plug samples were acquired from 30-meter-long sandstone and carbonate cores. Virtual plug porosity was calculated through correlations established between bulk density and CT numbers and compared with laboratory-measured helium porosity. A strong correlation was observed between the CT-derived and helium porosity values, as evidenced by a high coefficient of determination (R²).
This integrated approach not only streamlines early-stage porosity estimation but also significantly enhances SCAL sampling strategies, offering particular advantages in thinly laminated, fractured, and heterogeneous cores where physical plug acquisition can be operationally challenging.
In Japan, large-scale Carbon Capture and Storage (CCS) initiatives are currently on going, with several designated storage sites located in coastal regions. These environments present unique challenges for CCS monitoring, particularly in the application of geophysical methods. For instance, seismic methods for conformance monitoring must be adapted to shallow marine and transition zone settings, where data acquisition is often both technically demanding and costly. Additionally, potential impacts on local fisheries must be thoroughly evaluated and appropriately mitigated.
Tanase et al. (2021) showed the technical feasibility of monitoring in coastal environments for the Tomakomai CCS demonstration project. However, as Japan moves toward larger-scale, long-term practical CCS operations there is a growing need for more optimized and cost-effective monitoring strategies.
One promising solution for coastal CCS monitoring is Distributed Acoustic Sensing with Vertical Seismic Profiling (DAS-VSP). This technique enables imaging within the shoreline-adjacent transition zone and may reduce or even eliminate reliance on conventional Ocean Bottom Cables (OBCs). While permanently installed fiber, either behind casing or along tubing, is ideal for long-term surveillance, wireline DAS-VSP offers a practical alternative, allowing repeated surveys through existing wells. In 2022 and 2023, we conducted time-lapse wireline DAS-VSP experiments in wells completed over 50 years ago, successfully confirming the technical feasibility of this method for time-lapse monitoring. As such, this approach may serve as an effective optional or supplementary monitoring technique.
Another cost-effective approach involves 3D seismic surveying using Ocean Bottom Nodes (OBNs). Unlike OBC systems, OBNs eliminate the need for extensive cabling, thereby inherently reducing costs. To further enhance efficiency, we employed a parallel geometry for air-gun shooting. The results validated the effectiveness of this configuration, suggesting it as a valuable cost-saving solution, particularly when operating with a limited number of OBNs.
Given the substantial variability in site-specific conditions across coastal CCS projects, a flexible, multi-modal monitoring strategy is essential. Our research introduces alternative approaches that depart from conventional practices and demonstrates their technical feasibility. These findings expand the toolkit of cost-effective monitoring options available for coastal CCS deployment.
Reference:
Tanase, D., Saito, H., Niiro, R., Honda, T., Mori, A., Wada, Y., Higuchi, K., and Tanaka, J. (2021): Progress of CO2 injection and monitoring of the Tomakomai CCS Demonstration Project, Proceedings of the 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15.
Tanase et al. (2021) showed the technical feasibility of monitoring in coastal environments for the Tomakomai CCS demonstration project. However, as Japan moves toward larger-scale, long-term practical CCS operations there is a growing need for more optimized and cost-effective monitoring strategies.
One promising solution for coastal CCS monitoring is Distributed Acoustic Sensing with Vertical Seismic Profiling (DAS-VSP). This technique enables imaging within the shoreline-adjacent transition zone and may reduce or even eliminate reliance on conventional Ocean Bottom Cables (OBCs). While permanently installed fiber, either behind casing or along tubing, is ideal for long-term surveillance, wireline DAS-VSP offers a practical alternative, allowing repeated surveys through existing wells. In 2022 and 2023, we conducted time-lapse wireline DAS-VSP experiments in wells completed over 50 years ago, successfully confirming the technical feasibility of this method for time-lapse monitoring. As such, this approach may serve as an effective optional or supplementary monitoring technique.
Another cost-effective approach involves 3D seismic surveying using Ocean Bottom Nodes (OBNs). Unlike OBC systems, OBNs eliminate the need for extensive cabling, thereby inherently reducing costs. To further enhance efficiency, we employed a parallel geometry for air-gun shooting. The results validated the effectiveness of this configuration, suggesting it as a valuable cost-saving solution, particularly when operating with a limited number of OBNs.
Given the substantial variability in site-specific conditions across coastal CCS projects, a flexible, multi-modal monitoring strategy is essential. Our research introduces alternative approaches that depart from conventional practices and demonstrates their technical feasibility. These findings expand the toolkit of cost-effective monitoring options available for coastal CCS deployment.
Reference:
Tanase, D., Saito, H., Niiro, R., Honda, T., Mori, A., Wada, Y., Higuchi, K., and Tanaka, J. (2021): Progress of CO2 injection and monitoring of the Tomakomai CCS Demonstration Project, Proceedings of the 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15.
The oil and gas industry is undergoing a significant transformation fueled by the rapid advancements of digital technologies. The integration of advanced digitalization tools and artificial intelligence (AI) methodologies is revolutionizing the field of geoscience, supporting optimization of complex geological workflows, unlocking new insights from diverse data sources, and reactivating legacy assets. This digital transformation deepens the understanding of subsurface reservoirs and empowers more informed, efficient, and effective decision-making. By leveraging multi-scale, multimodal digital imaging and AI methods, we can analyze vast datasets and extract valuable insights from millions of geological rock samples and analytical reports. This paper highlights the potential of digital transformation to revolutionize oil and gas geoscience, with the goal of transforming exploration workflows and reservoir characterization outcomes.
The discovery of oil in Saudi Arabia in the 1930s marked the beginning of a rich legacy of geological knowledge, resulting in a vast and diverse repository of operational data and geological sample sets. However, the unstructured and variable nature of this legacy data has hindered its full potential, limiting its accessibility and integration. To overcome this challenge, we employed Generative AI (GenAI) technologies to transform the original data into a structured database, utilizing Optical Character Recognition (OCR) models and Retrieval-Augmented Generation (RAG) pipelines. This approach enabled efficient access and utilization of the information, including the application of AI-driven analytics. Concurrently, we digitized and analyzed a vast collection of subsurface rock samples to create a 'Digital Rock,' generating high-fidelity digital twins of the rock samples and predicting rock properties via simulations of physical and chemical processes at the pore scale.
Our digital transformation initiatives have significantly enhanced the efficiency of exploration and petroleum system analysis, saving thousands of man-hours in the search and retrieval of geological information. The Digital Rock has unlocked novel insights into complex petroleum system elements, including microporous, bioturbated, thin-bedded, tight sandstones, hot shales, and other challenging formations. Two exemplary case studies demonstrate the potential of digital transformation: (1) the regularization of over 90 years of biostratigraphic analysis datasets using GenAI, and (2) the assessment of flow heterogeneity in mega-porous reservoirs utilizing a multi-scale digital imaging workflow. These applications underscore the transformative potential of digital technologies in geoscience, enabling more accurate and efficient reservoir characterization.
The integration of rock sample images with corresponding interpretation reports has enabled the creation of a comprehensive and robust database, providing a solid foundation for training and validating AI models. This multidisciplinary approach has significantly enhanced our understanding of the subsurface environment. The synergistic relationship between geological data, interpretation, and AI-modeling has the potential to revolutionize the field of geoscience, enabling more informed decision-making for effective exploration and field development strategies.
The discovery of oil in Saudi Arabia in the 1930s marked the beginning of a rich legacy of geological knowledge, resulting in a vast and diverse repository of operational data and geological sample sets. However, the unstructured and variable nature of this legacy data has hindered its full potential, limiting its accessibility and integration. To overcome this challenge, we employed Generative AI (GenAI) technologies to transform the original data into a structured database, utilizing Optical Character Recognition (OCR) models and Retrieval-Augmented Generation (RAG) pipelines. This approach enabled efficient access and utilization of the information, including the application of AI-driven analytics. Concurrently, we digitized and analyzed a vast collection of subsurface rock samples to create a 'Digital Rock,' generating high-fidelity digital twins of the rock samples and predicting rock properties via simulations of physical and chemical processes at the pore scale.
Our digital transformation initiatives have significantly enhanced the efficiency of exploration and petroleum system analysis, saving thousands of man-hours in the search and retrieval of geological information. The Digital Rock has unlocked novel insights into complex petroleum system elements, including microporous, bioturbated, thin-bedded, tight sandstones, hot shales, and other challenging formations. Two exemplary case studies demonstrate the potential of digital transformation: (1) the regularization of over 90 years of biostratigraphic analysis datasets using GenAI, and (2) the assessment of flow heterogeneity in mega-porous reservoirs utilizing a multi-scale digital imaging workflow. These applications underscore the transformative potential of digital technologies in geoscience, enabling more accurate and efficient reservoir characterization.
The integration of rock sample images with corresponding interpretation reports has enabled the creation of a comprehensive and robust database, providing a solid foundation for training and validating AI models. This multidisciplinary approach has significantly enhanced our understanding of the subsurface environment. The synergistic relationship between geological data, interpretation, and AI-modeling has the potential to revolutionize the field of geoscience, enabling more informed decision-making for effective exploration and field development strategies.
In Japan, large-scale Carbon Capture and Storage (CCS) initiatives are currently on going, with several designated storage sites located in coastal regions. These environments present unique challenges for CCS monitoring, particularly in the application of geophysical methods. For instance, seismic methods for conformance monitoring must be adapted to shallow marine and transition zone settings, where data acquisition is often both technically demanding and costly. Additionally, potential impacts on local fisheries must be thoroughly evaluated and appropriately mitigated.
Tanase et al. (2021) showed the technical feasibility of monitoring in coastal environments for the Tomakomai CCS demonstration project. However, as Japan moves toward larger-scale, long-term practical CCS operations there is a growing need for more optimized and cost-effective monitoring strategies.
One promising solution for coastal CCS monitoring is Distributed Acoustic Sensing with Vertical Seismic Profiling (DAS-VSP). This technique enables imaging within the shoreline-adjacent transition zone and may reduce or even eliminate reliance on conventional Ocean Bottom Cables (OBCs). While permanently installed fiber, either behind casing or along tubing, is ideal for long-term surveillance, wireline DAS-VSP offers a practical alternative, allowing repeated surveys through existing wells. In 2022 and 2023, we conducted time-lapse wireline DAS-VSP experiments in wells completed over 50 years ago, successfully confirming the technical feasibility of this method for time-lapse monitoring. As such, this approach may serve as an effective optional or supplementary monitoring technique.
Another cost-effective approach involves 3D seismic surveying using Ocean Bottom Nodes (OBNs). Unlike OBC systems, OBNs eliminate the need for extensive cabling, thereby inherently reducing costs. To further enhance efficiency, we employed a parallel geometry for air-gun shooting. The results validated the effectiveness of this configuration, suggesting it as a valuable cost-saving solution, particularly when operating with a limited number of OBNs.
Given the substantial variability in site-specific conditions across coastal CCS projects, a flexible, multi-modal monitoring strategy is essential. Our research introduces alternative approaches that depart from conventional practices and demonstrates their technical feasibility. These findings expand the toolkit of cost-effective monitoring options available for coastal CCS deployment.
Reference:
Tanase, D., Saito, H., Niiro, R., Honda, T., Mori, A., Wada, Y., Higuchi, K., and Tanaka, J. (2021): Progress of CO2 injection and monitoring of the Tomakomai CCS Demonstration Project, Proceedings of the 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15.
Tanase et al. (2021) showed the technical feasibility of monitoring in coastal environments for the Tomakomai CCS demonstration project. However, as Japan moves toward larger-scale, long-term practical CCS operations there is a growing need for more optimized and cost-effective monitoring strategies.
One promising solution for coastal CCS monitoring is Distributed Acoustic Sensing with Vertical Seismic Profiling (DAS-VSP). This technique enables imaging within the shoreline-adjacent transition zone and may reduce or even eliminate reliance on conventional Ocean Bottom Cables (OBCs). While permanently installed fiber, either behind casing or along tubing, is ideal for long-term surveillance, wireline DAS-VSP offers a practical alternative, allowing repeated surveys through existing wells. In 2022 and 2023, we conducted time-lapse wireline DAS-VSP experiments in wells completed over 50 years ago, successfully confirming the technical feasibility of this method for time-lapse monitoring. As such, this approach may serve as an effective optional or supplementary monitoring technique.
Another cost-effective approach involves 3D seismic surveying using Ocean Bottom Nodes (OBNs). Unlike OBC systems, OBNs eliminate the need for extensive cabling, thereby inherently reducing costs. To further enhance efficiency, we employed a parallel geometry for air-gun shooting. The results validated the effectiveness of this configuration, suggesting it as a valuable cost-saving solution, particularly when operating with a limited number of OBNs.
Given the substantial variability in site-specific conditions across coastal CCS projects, a flexible, multi-modal monitoring strategy is essential. Our research introduces alternative approaches that depart from conventional practices and demonstrates their technical feasibility. These findings expand the toolkit of cost-effective monitoring options available for coastal CCS deployment.
Reference:
Tanase, D., Saito, H., Niiro, R., Honda, T., Mori, A., Wada, Y., Higuchi, K., and Tanaka, J. (2021): Progress of CO2 injection and monitoring of the Tomakomai CCS Demonstration Project, Proceedings of the 15th International Conference on Greenhouse Gas Control Technologies, GHGT-15.
Ibrahim Olgun Ugurlu
Speaker
Senior Sedimentologist & Whole-Core CT Rock Imaging Lead
Turkish Petroleum Corporation
Turkiye
Core data has traditionally served as the foundational reference for formation evaluation by providing direct measurements of reservoir petrophysical properties. Following core retrieval, plugs intended for special core analysis (SCAL) are preserved, while routine analysis plugs must undergo extensive cleaning to remove hydrocarbons and salts, typically through Soxhlet extraction using hot toluene and methanol. This cleaning process can extend over several weeks, depending on factors such as rock permeability, pore structure complexity, and the type of hydrocarbons present. In this context, emerging digital imaging technologies, such as whole-core computed tomography (CT) scanning, have become particularly valuable by offering a rapid, nondestructive method for detailed core characterization and representative sample selection.
Whole-core CT imaging is a widely adopted technique in the oil and gas industry for assessing the internal structure of core samples. It enables quick visualization of core tubes for sample selection and provides critical geological and petrophysical insights, including the identification of fractures, facies transitions, and porosity variations. A major advantage of X-ray CT imaging lies in its ability to generate continuous, high-resolution images coupled with quantitative data essential for core evaluation. In this study, standard whole-core CT scanning was conducted at a single high-energy setting (140 kV) to primarily capture density variations along the core length, and the extracted data were utilized for porosity prediction. These continuous datasets are particularly valuable during the early phases of core analysis programs for characterizing reservoir heterogeneity and optimizing SCAL sample selection.
The primary objective of this study is to accelerate porosity estimation by integrating whole-core CT scanning, 3D virtual core plug acquisition, and quantitative CT data analysis, while also examining CT responses across different Winland r35 rock types.
The 3D virtual core plugging technique, a relatively recent innovation, involves creating digital replicas of core plugs of any desired diameter using specialized software. In this study, approximately seventy virtual plug samples were acquired from 30-meter-long sandstone and carbonate cores. Virtual plug porosity was calculated through correlations established between bulk density and CT numbers and compared with laboratory-measured helium porosity. A strong correlation was observed between the CT-derived and helium porosity values, as evidenced by a high coefficient of determination (R²).
This integrated approach not only streamlines early-stage porosity estimation but also significantly enhances SCAL sampling strategies, offering particular advantages in thinly laminated, fractured, and heterogeneous cores where physical plug acquisition can be operationally challenging.
Whole-core CT imaging is a widely adopted technique in the oil and gas industry for assessing the internal structure of core samples. It enables quick visualization of core tubes for sample selection and provides critical geological and petrophysical insights, including the identification of fractures, facies transitions, and porosity variations. A major advantage of X-ray CT imaging lies in its ability to generate continuous, high-resolution images coupled with quantitative data essential for core evaluation. In this study, standard whole-core CT scanning was conducted at a single high-energy setting (140 kV) to primarily capture density variations along the core length, and the extracted data were utilized for porosity prediction. These continuous datasets are particularly valuable during the early phases of core analysis programs for characterizing reservoir heterogeneity and optimizing SCAL sample selection.
The primary objective of this study is to accelerate porosity estimation by integrating whole-core CT scanning, 3D virtual core plug acquisition, and quantitative CT data analysis, while also examining CT responses across different Winland r35 rock types.
The 3D virtual core plugging technique, a relatively recent innovation, involves creating digital replicas of core plugs of any desired diameter using specialized software. In this study, approximately seventy virtual plug samples were acquired from 30-meter-long sandstone and carbonate cores. Virtual plug porosity was calculated through correlations established between bulk density and CT numbers and compared with laboratory-measured helium porosity. A strong correlation was observed between the CT-derived and helium porosity values, as evidenced by a high coefficient of determination (R²).
This integrated approach not only streamlines early-stage porosity estimation but also significantly enhances SCAL sampling strategies, offering particular advantages in thinly laminated, fractured, and heterogeneous cores where physical plug acquisition can be operationally challenging.
