Machine learning methods for estimating permeability of a reservoir
Author:
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
https://link.springer.com/content/pdf/10.1007/s13198-022-01655-9.pdf
Reference90 articles.
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2. Afify WE, Hassan AHI (2010) Permeability and porosity prediction from wireline logs using neuro-fuzzy technique. Ozean J Appl Sci 3:157–175
3. Afshin D, Behnam M, Taraneh JB, Mahmoud H (2017) Application of production logging tools in estimating the permeability of fractured carbonated reservoirs: A comparative study. J Pet Gas Eng 8:36–41. https://doi.org/10.5897/JPGE2016.0245
4. Aghli G, Moussavi-Harami R, Mortazari S, Mohammadian R (2019) Evaluation of new method for estimation of fracture parameters using conventional petrophysical logs and ANFIS in the carbonate heterogenous reservoirs. J Pet Sci Eng 172:1092–1102. https://doi.org/10.1016/j.petrol.2018.09.0
5. Aguilar C, Govea H, Pdvsa P, Rincón G (2014) Hydraulic unit determination and permeability prediction based on flow. In: Paper presented at the SPE Latin America and Caribbean Petroleum Engineering Conference, Maracaibo, Venezuela, May 2014. https://doi.org/10.2118/169307-MS
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