Applications of Data Mining in Hydrocarbon Exploration, Constraints on Geology and Petroleum Reservoir
Rajesh Kanna1, Sivasankar P2, Kalpana S3
1Rajesh Kanna*, Department of Petroleum Engineering, Academy of Maritime Education and Training (AMET), Chennai, India.
2Sivasankar P, Department of Petroleum Engineering, Indian Institute of Petroleum and Energy, Visakhapatnam, India.
3Kalpana S, Department of Physics, Academy of Maritime Education and Training (AMET), Chennai, India.
Manuscript received on April 02, 2020. | Revised Manuscript received on April 21, 2020. | Manuscript published on May 30, 2020. | PP: 921-924 | Volume-9 Issue-1, May 2020. | Retrieval Number : F9446038620/2020©BEIESP | DOI: 10.35940/ijrte.F9446.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the present scenario, low oil prices and explorations shave triggered energy industries to look into cost depression of supply chains vibrantly. Advanced and new technology had been identified and experimentally considered for new hydrocarbon exploration prevailing data mining. Geoscience and geophysical problems are dominated as data obtained for mining process is enforced for geology and reservoir issues and properties. Our present research deals about understanding the geological problems clearly analysed in the literature and with few experiments, a variety of data mining methods for the outcome has been concluded, which provides solution for a betterment understanding about gas and oil exploration with that of data miners and the geoscientists. Collection of data for various hydrocarbon wells has been addressed with seismic surveys, for identification of source, segregating and forecasting using iteration methods and neural networks had been discussed for betterment exploration of new wells without any constraints.
Keywords: Hydrocarbon, CBM, Recovery, Shale, Geoscience.
Scope of the Article: Data Mining