Amadasu, Osaretin Steven (2024) AI-Enhanced Monitoring of Microbial Activity in Hydraulic Fracturing Fluids. International Journal of Micro Biology, Genetics and Monocular Biology Research, 7 (1). pp. 21-41. ISSN 2059-9609(Print),2059-9617(Online)
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Abstract
Hydraulic fracturing, commonly known as fracking, is a transformative technology that has significantly advanced the oil and gas industry's ability to extract hydrocarbons from unconventional reservoirs, such as shale formations. This process involves injecting high-pressure fluids into rock formations to create fractures, facilitating the flow of trapped hydrocarbons to the surface. Despite its effectiveness, hydraulic fracturing is a complex process involving various chemical and physical interactions, including the influence of microbial activity within the fracturing fluids. Microbial activity is of particular concern as it can lead to the degradation of fracturing fluids and the biocorrosion of infrastructure, which in turn can reduce the efficiency of the extraction process and pose environmental and operational risks.Traditionally, the oil and gas industry has relied on conventional methods to monitor microbial activity in hydraulic fracturing fluids. These methods typically involve culture-based techniques, molecular analysis, and microscopy, which, while valuable, often suffer from several limitations. Primarily, these techniques are reactive rather than proactive, providing insights only after microbial activity has already occurred. Additionally, they are generally time-consuming and unable to offer real-time data, leading to delayed responses and potential operational inefficiencies. The lack of real-time monitoring also means that microbial issues, such as biofilm formation and microbial-induced corrosion (MIC), may go unnoticed until they have caused significant damage, resulting in costly maintenance and downtime.In response to these challenges, the integration of Artificial Intelligence (AI) into microbial monitoring systems represents a promising advancement. AI has the capability to process vast amounts of data, identify patterns, and make real-time predictions, which can significantly enhance the monitoring and management of microbial activity in hydraulic fracturing fluids. This paper explores how AI-driven models can be employed to provide real-time, predictive insights into microbial behavior, allowing operators to take proactive measures to mitigate risks. By analyzing data from sensors placed in the field, AI models can detect early signs of microbial growth, predict the formation of biofilms, and estimate the potential for biocorrosion. This proactive approach enables more efficient use of biocides, reduces the likelihood of equipment failure, and minimizes the environmental impact of fracking operations.The integration of AI in microbial monitoring is not just about improving operational efficiency; it also has significant implications for environmental sustainability. Hydraulic fracturing has faced scrutiny due to its environmental impact, particularly concerning water usage and potential contamination. By optimizing microbial management through AI, the industry can reduce the need for chemical additives, lower the risk of environmental contamination, and enhance the overall sustainability of hydraulic fracturing practices. This paper presents a comprehensive examination of the methodologies for implementing AI in microbial monitoring within hydraulic fracturing fluids. It begins with a detailed review of existing literature on microbial activity in fracking, highlighting the limitations of traditional monitoring techniques and the potential benefits of AI. The integration of AI into microbial monitoring systems offers a transformative approach to managing the complexities of hydraulic fracturing. By providing real-time, predictive insights, AI can help operators optimize their processes, reduce environmental risks, and enhance the overall effectiveness of fracking operations. This paper contributes to the growing body of knowledge on AI applications in the oil and gas industry, offering valuable insights for industry professionals, researchers, and policymakers interested in the future of hydraulic fracturing and microbial management.
| Item Type: | Article |
|---|---|
| Subjects: | R Medicine > R Medicine (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 27 Sep 2024 16:14 |
| Last Modified: | 27 Sep 2024 16:14 |
| URI: | https://tudr.org/id/eprint/3427 |
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