A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks-Reference-Cited by-同舟云学术

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A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks

Published:2021-11 Issue: Volume:95 Page:104104
ISSN:1875-5100
Container-title:Journal of Natural Gas Science and Engineering
language:en
Short-container-title:Journal of Natural Gas Science and Engineering

Author:

Etesami Danial,Zhang W.J.,Hadian Mohsen

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference59 articles.

1. Petroleum Rock Mechanics;Aadnoy,2019

2. Drilling rate of penetration prediction of high-angled wells using artificial neural networks;Abbas;ASME. J. Energy Resour. Technol.,2019

3. New artificial neural networks model for predicting rate of penetration in deep shale formation;Abdulmalek;Sustainability,2019

4. Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: application of artificial intelligence;Ahmadi;J. Petrol. Sci. Eng.,2014

5. Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration;Ahmadi;Int. J. Low Carbon Technol.,2016

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