Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga oilfield, Algeria-Reference-Cited by-同舟云学术

Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga oilfield, Algeria

Published:2023-04 Issue: Volume:223 Page:211511
ISSN:2949-8910
Container-title:Geoenergy Science and Engineering
language:en
Short-container-title:Geoenergy Science and Engineering

Author:

Ouladmansour Abdelhamid^ORCID,Ameur-Zaimeche Ouafi^ORCID,Kechiched Rabah^ORCID,Heddam Salim^ORCID,Wood David A.^ORCID

Publisher

Elsevier BV

Reference61 articles.

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3. Real-time static Poisson's ratio prediction of vertical complex lithology from drilling parameters using artificial intelligence models;Ahmed;Arabian J. Geosci.,2021

4. Estimation of reservoir porosity from drilling parameters using artificial neural networks;Al-AbdulJabbar;Petrophysics-The SPWLA J. Form. Eval. Reserv. Descrip.,2020

5. Workflow to predict wellhead choke performance during multiphase flow using machine learning;Alarifi;J. Petrol. Sci. Eng.,2022

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