Amadasu, Osaretin Steven (2024) AI-Enhanced Metagenomics for Reservoir Characterization: A New Frontier in Oil and Gas Exploration. International Journal of Petroleum and Gas Engineering Research, 7 (2). pp. 1-16. ISSN ISSN 2514-9253(Print), ISSN 2514-9261 (Online)
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Abstract
The integration of Artificial Intelligence (AI) with metagenomics heralds a new era in the field of reservoir characterization, offering a paradigm shift in how we understand and manage hydrocarbon reservoirs. This paper delves into the burgeoning potential of AI-enhanced metagenomic techniques, which stand at the forefront of innovation in the oil and gas industry. By harnessing the power of AI, researchers and engineers can now decode the complex biological tapestry woven by microbial communities within these reservoirs. This integration facilitates a more nuanced and comprehensive analysis than traditional methods alone, leading to a richer, multi-dimensional understanding of reservoir ecosystems.AI's advanced algorithms and machine learning capabilities enable the processing and interpretation of vast and intricate metagenomic datasets, which were previously intractable. This allows for the identification of novel biomarkers, the elucidation of microbial interactions, and the prediction of functional capabilities within the microbial consortia. Consequently, this enhanced analytical prowess translates into more accurate predictions of reservoir properties, such as porosity, permeability, and fluid composition, as well as the behavior of these complex systems under various operational scenarios.Furthermore, the paper investigates how AI-augmented metagenomics can illuminate the role of microorganisms in biogeochemical processes, such as biodegradation and souring, which are critical to reservoir management and enhanced oil recovery strategies. It also examines the implications of this technology for environmental stewardship, highlighting its potential to mitigate the ecological impact of hydrocarbon extraction. In essence, the fusion of AI with metagenomics embodies a significant advancement in reservoir characterization. It not only enhances our ability to characterize the subsurface realm but also promises to refine predictive modeling and decision-making processes, ultimately leading to more efficient and sustainable exploitation of natural resources. This paper aims to provide a comprehensive overview of the current state of AI-enhanced metagenomic approaches, discuss their practical applications, and offer a vision for their future development within the oil and gas sector.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 23 May 2024 10:17 |
| Last Modified: | 23 May 2024 10:17 |
| URI: | https://tudr.org/id/eprint/3018 |
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