Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Mudunuru, Maruti K.; Ahmmed, Bulbul; Rau, Elisabeth G.; Vesselinov, Velimir V.; Karra, Satish

doi:10.3390/en16073098

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Among them, only one integrates multi-criteria evaluation with ML tools such as Fuzzy prediction modeling [33]. Another study that uses ML tools (particularly k-means clustering) in geothermal resource exploration was published by Mudunuru et al [34]. The ML techniques have been focused mainly on integrating surface geochemistry data for reservoir temperature classification [35], selecting hidden signatures representative of the geothermal resource, and choosing potential geothermal sites [36].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools

2023

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The study evaluates the geothermal energy potential of two depleted oil and gas reservoirs representing two different lithostratigraphic formations—the carbonate formation of the Visean age from the basement of the Carpathian Flysch and the Rotliegend sandstone formation from the Eastern part of the Foresudetic Monocline, Poland. Advanced modeling techniques were employed to analyze the studied formations’ heat, storage, and transport properties. The obtained results were then used to calculate the heat in place (HIP) and evaluate the recoverable heat (Hrec) for both water and CO2 as working fluids, considering a geothermal system lifetime of 50 years. The petrophysical parameters and Hrec were subsequently utilized in the generalized c-means (GFCM) clustering analysis, which helped to identify the plays with the greatest geothermal potential within the studied formations. The central block emerged as the most promising area for the studied carbonate formation. It exhibited significant potential for heat production, with Hrec values of approximately ~4036 and 932 MW when H2O and CO2 were used as working fluids, respectively. The central block has three wells that can be easily adapted for geothermal production. The area, however, may require permeability enhancement techniques to increase reservoir permeability. Two prospective zones were determined for the analyzed Rotliegend sandstone formation: one in the NW region and the other in the SE region. In the NW region, the estimated Hrec was 233,855 MW and 44,018 MW, while in the SE region, it was 257,457 MW and 48,231 MW, using H2O and CO2 as working fluids, respectively. Both areas have high porosity and permeability, providing good storage and transport properties for the working fluid, and abundant wells that can be configured for multiple injection-production systems. When comparing the efficiency of geothermal systems, the water-driven system in the Visean carbonate formation turned out to be over four times more efficient than the CO2-driven one. Furthermore, in the case of the Rotliegend sandstone formation, it was possible to access over five times more heat using water-driven system.

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Section: Introductionmentioning

confidence: 99%

Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools

2023

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Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Al‐Fakih,

Abdulraheem,

Kaka

2024

Deep Underground Science and Engineering

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This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

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Hybridizing K-means clustering algorithm with harmony search and artificial bee colony optimizers for intelligence mineral prospectivity mapping

et al. 2023

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Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Cited by 5 publications

References 29 publications

Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools

Assessing the Geothermal Potential of Selected Depleted Oil and Gas Reservoirs Based on Geological Modeling and Machine Learning Tools

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Hybridizing K-means clustering algorithm with harmony search and artificial bee colony optimizers for intelligence mineral prospectivity mapping

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