The geothermal magmatic system at the northern Rahat volcanic field, Saudi Arabia, revealed from 3D magnetotelluric inversion

Aboud, Essam; Arafa-Hamed, Tarek; Alqahtani, Faisal; Marzouk, Hossam; Elbarbary, Samah; Abdulfaraj, Murad; El-Masry, Nabil

doi:10.1016/j.jvolgeores.2023.107794

Cited by 8 publications

(7 citation statements)

References 49 publications

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“…One-dimensional inversion is conducted using the Bostick and Occam algorithms in the WinGLink application. The Bostick curve is in the form of apparent resistivity, while the Occam curve is in the form of real resistivity [27]. Occam's algorithm is the main emphasis of this model since it is more accurate and has a smaller error value than Bostick's algorithm [28].…”

Section: One-dimensional and Two-dimensional Inversionmentioning

confidence: 99%

Identification of shale gas existence based on 2-dimensional magnetotelluric data in the Kutai basin, East Kalimantan, Indonesia

Putri,

Hawan,

Hapsoro

et al. 2024

J. Phys.: Conf. Ser.

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Research on unconventional methods in the shale gas exploration process has begun to be developed to meet the demand for oil and natural gas sustainability. The research area is located in the Kutai Basin, East Kalimantan, Indonesia. The current study from the Geological Survey Center shows that the Kutai Basin has 46.79 TCF of shale gas potential. The magnetotelluric (MT) method was applied in this study to describe the distribution of subsurface resistivity values. The inversion schemes performed in the MT method are 1-dimensional and 2-dimensional inversions. In this study, there are 8 measurement stations in one line. The presence of low resistivity values or conductive zones identifies the potential for black shale layers. The potential for shale gas is suspected to be detected at station KT29, which is included in the Pamaluan Formation, and at stations KT12, KT31, KT13, and KT15, which are included in the Pulaubalang Formation, because it is associated with source rock from petroleum in the Kutai Basin system, as supported by previous research data.

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Section: One-dimensional and Two-dimensional Inversionmentioning

confidence: 99%

Identification of shale gas existence based on 2-dimensional magnetotelluric data in the Kutai basin, East Kalimantan, Indonesia

Putri,

Hawan,

Hapsoro

et al. 2024

J. Phys.: Conf. Ser.

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“…Geothermal resources in Saudi Arabia are promising. Several exploration campaigns and research studies have investigated Saudi Arabia's geothermal resources for decades [55][56][57][58][59][60][61], with a couple focusing on the Al-Lith area in southwestern Saudi Arabia [62][63][64][65]. The Al-Lith area has four hot springs with surface temperatures ranging from 41 • C to 96 • C, with Ain Al-Harrah having the highest.…”

Section: Geothermalmentioning

confidence: 99%

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

Barajas-Olalde,

Adams,

Curcio

et al. 2023

Minerals

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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.

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“…Within the Arabian Plate, it is the biggest of the fifteen Harrats or volcanic fields [22][23][24]. It comprises over 900 volcanic events across an area of over 20,000 km 2 [7,25]. From north to south, it comprises the smaller volcanic fields of Harrat Turrah, Rukhq, Abu Rashid, and Bani Abdullah (Figure 2).…”

Section: Geological Setting and Study Area Descriptionmentioning

confidence: 99%

“…The better-developed silicic rocks and localized volcanic and plutonic basalt sequences in the western portion of the Arabian Plate provide evidence of Late Cenozoic volcanism [24]. The Arabian Plate has experienced at least 21 eruptions during the last 1500 years; the last one was in Yemen in 1937 [7]. The Afar plume (located at the triple junction of the Gulf of Aden, the Red Sea, and East African rift systems) has been interpreted as being connected to eruptive processes on the Arabian Plate that are older than ten million years.…”

Section: Geological Setting and Study Area Descriptionmentioning

confidence: 99%

“…A literature survey reported that numerous studies have been performed to investigate HR geothermal potential using various techniques, which have included: geochemical analysis, major-elements and trace-elements analysis, geothermal gradient analysis, geothermal fluid characterization, gravity and magnetic analysis, structural trend analysis, and geothermal well drilling [6][7][8][9][10]. However, previous studies have not fully mapped One reason for the low percentage of geothermal resource usage is the limited availability of high-enthalpy geothermal resources and the high costs and risks associated with developing geothermal fields.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Techniques in Predicting Bottom Hole Temperature and Remote Sensing for Assessment of Geothermal Potential in the Kingdom of Saudi Arabia

Alqahtani,

Ehsan,

Abdulfarraj

et al. 2023

Sustainability

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The global demand for energy is increasing rapidly due to population growth, urbanization, and industrialization, as well as to meet the desire for a higher standard of living. However, environmental concerns, such as air pollution from fossil fuels, are becoming limiting factors for energy sources. Therefore, the appropriate and sustainable solution is to transition towards renewable energy sources to meet global energy demands by using environmentally friendly sources, such as geothermal. The Harrat Rahat volcanic field, located in the western region of the Kingdom of Saudi Arabia (KSA), gets more attention due to its geothermal potential as a viable site for geothermal energy exploration due to its high enthalpy. The prime objective of this study is to present up-to-date and comprehensive information on the utilization of borehole temperature and remote sensing data to identify the most prospective zones with significant geothermal activity favorable for exploration and drilling. A brief description of the selected wells and the methodology used to determine the petrophysical parameters relevant to the geothermal potential assessment are presented. Special emphasis is given to gamma-ray ray and temperature logs for calculating heat production and the geothermal gradient. The effectiveness of various machine learning techniques are assessed throughout this study for predicting the temperature-at-depth to evaluate the suitability of employing machine learning models for temperature prediction, and it is found that XG Boost provided excellent results. It can be observed that some linear anomalies can be traced in the NW, trending on the west side of the Harrat volcanic field based on magnetic data interpretation. The land surface temperature in 2021 exhibited higher temperatures compared to 2000, suggesting potential volcanic activity in the subsurface. It is concluded that the integration of remote sensing data with subsurface data provides the most reliable results.

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The geothermal magmatic system at the northern Rahat volcanic field, Saudi Arabia, revealed from 3D magnetotelluric inversion

Cited by 8 publications

References 49 publications

Identification of shale gas existence based on 2-dimensional magnetotelluric data in the Kutai basin, East Kalimantan, Indonesia

Identification of shale gas existence based on 2-dimensional magnetotelluric data in the Kutai basin, East Kalimantan, Indonesia

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

Machine Learning Techniques in Predicting Bottom Hole Temperature and Remote Sensing for Assessment of Geothermal Potential in the Kingdom of Saudi Arabia

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