Open Access
Issue
E3S Web Conf.
Volume 367, 2023
The 2022 International Symposium of the Society of Core Analysts (SCA 2022)
Article Number 01004
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202336701004
Published online 31 January 2023
  1. S.B. Suslick, D. Schiozer, M.R. Rodriguez. Uncertainty and Risk Analysis in Petroleum Exploration and Production. Terrae 6(1): 30-21 (2009). https://www.ige.unicamp.br/terrae/V6/PDF-N6/T-a3i.pdf [Google Scholar]
  2. C. McPhee, J. Reed, I. Zubizarreta. Core Analysis – A Best Practice Guide. https://app.knovel.com/hotlink/toc/id:kpCAABPG03/coreanalysis-best-practice/core-analysis-best-practice [Google Scholar]
  3. A. Sircar, K. Yadav, K. Rayavarapu, N. Bist, H. Oza. Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research 6, 379-391, (2021). [CrossRef] [Google Scholar]
  4. Y.R. Busaleh, A Abdulraheem, T. Okasha. Prediction of Capillary Pressure for Oil Carbonate Reservoirs by Artificial Intelligence Technique. SPE Asia Pacific Oil and Gas Conference Exhibition. (2016). https://doi.org/10.2118/182173-MS [Google Scholar]
  5. M. Jamshidian, M. Mansouri Zadehm M. Hadian, R. Moghadasi, O. Mohammadzadeh. A Novel Estimation Method for Capillary Pressure Curves Based on Routine Core Analysis Data Using Artificial Neural Networks Optimized by Cuckoo Algorithm – A Case Study. (2018). Fuel, 220, 363–378. https://doi.org/10.1016/j.fuel.2018.01.099 [CrossRef] [Google Scholar]
  6. A.A. Kasha, A. Sakhaee-Pour, I.A. Hussein. ‘Machine Learning for Capillary Pressure Estimation.’ SPE Res Eval & Eng 25. (2022): 1-20. doi: https://doi-org.qe2aproxy.mun.ca/10.2118/208579-PA [CrossRef] [Google Scholar]
  7. T. Mitchell. Machine Learning. New York: McGraw Hill. (1997). OCLC 36417892. [Google Scholar]
  8. Mathworks Help Center. Machine Learning in MATLAB https://www.mathworks.com/help/stats/machine-learning-in-matlab.html [Google Scholar]
  9. T. Hastie, R. Tibshirani, J. Friedman. The Eléments of Statistical Learning. Springer New York. https://doi.org/10.1007/978-0-387-84858-7_12 [Google Scholar]
  10. A. Shmilovici. Support Vector Machines. In : O. Maimon, L. Rokach. (eds) Data Mining and Knowledge Discovery Handbook. (2005). Springer, Boston, MA. https://doi-org.qe2a-proxy.mun.ca/10.1007/0-387-25465X_12 [CrossRef] [Google Scholar]
  11. Rodríguez-Pérez, R., Bajorath, J. Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery. J Comput Aided Mol Des (2022). https://doi-org.qe2aproxy.mun.ca/10.1007/s10822-022-00442-9 [Google Scholar]
  12. K. Fawagreh, M.M. Gaber, E. Elyan. Random forests: from early developments to recent advancements. System Science and Control Engineering. Volume 2. (2014). https://doi-org.qe2aproxy.mun.ca/10.1080/21642583.2014.956265 [Google Scholar]
  13. P. Forbes. Simple and Accurate Methods for Converting Centrifuge Data into Drainage and Imbibition Capillary Pressure Curves. SPWLA-1994-V35n4a3, 35(04), 13. (1994). [Google Scholar]
  14. Z.A. Chen, D.W. Ruth. Measurement and Interpretation of Centrifuge Capillary Pressure Curves-The SCA Survey Data. The Log Analyst, 36(05), 13. (1995). [Google Scholar]
  15. P. Forbes. Centrifuge Data Analysis Techniques : An SCA Survey on the Calculation of Drainage Capillary Pressure Curves from Centrifuge Measurements. 20. (1997). [Google Scholar]
  16. J.E. Nordtvedt, K. Kolltvelt. Capillary Pressure Curves From Centrifuge Data by Use of Spline Functions. SPE19019-PA, 6(04), 497–501. https://doi.org/10.2118/19019PA (1991). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.