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Informative Article | Data & Knowledge Engineering | India | Volume 11 Issue 3, March 2022 | Rating: 4.9 / 10
Enhancing Seismic Data Interpretation through Unsupervised Machine Learning and Data Analytics for Improved Reservoir Characterization
Abstract: Interpreting seismic data is essential for characterizing reservoirs, as it helps in decoding under-the-earth geological formations and refining the search and extraction of hydrocarbons. Yet, the growing scale and intricacy of seismic data introduces hurdles to the classic methods of interpretation. This paper introduces method using unsupervised machine learning and analytics to boost the interpretation of seismic data and enhance the characterization of reservoirs. Through the deployment of these unsupervised learning techniques, my strategy facilitates the uncovering of elusive geological formations, variations in rock bodies, and anomalies in fluids that might escape notice using traditional interpretation processes. The machine learning algorithms? capacity to autonomously extract features and recognize patterns makes it possible to discover reservoir segments, stratigraphic traps, and paths of fluid movement that were previously invisible. Additionally, incorporating data analytics techniques enables the smooth combination of varied data types, including well logs, output data, and petrophysical insights, with the seismic interpretations. This comprehensive tactic strengthens reservoir characterization, supporting more informed decisions in the development and optimization of fields. The document ends by touching on the prospective benefits the suggested method could bring to the petroleum industry, underscoring its potential to cut down on interpretation timelines, diminish subjectivity, and amplify our grasp on intricate reservoir systems. The employment of unsupervised machine learning and data analytics in the interpretation of seismic data marks a pivotal advance in adopting cutting-edge technologies for better characterization of reservoirs and the enhancement of operational efficacy.
Keywords: seismic data interpretation, unsupervised machine learning, data analytics, reservoir characterization, self- organizing maps, clustering, automated feature extraction, pattern recognition, multi-disciplinary data integration, decision- making, field development, production optimization
Edition: Volume 11 Issue 3, March 2022,
Pages: 1583 - 1593