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dc.contributor.advisorHaddan, Abdelnour-
dc.contributor.authorLEHELLA, OTMAN-
dc.date.accessioned2025-12-18T10:48:07Z-
dc.date.available2025-12-18T10:48:07Z-
dc.date.issued2023-
dc.identifier.urihttps://dspace.univ-ouargla.dz/jspui/handle/123456789/39719-
dc.descriptionKASDI MERBAH UNIVERSITY – OUARGLA FACULTY OF HYDROCARBONS, RENEWABLE ENERGY AND EARTH AND UNIVERSE SCIENCES DEPARTMENT OF EARTH AND UNIVERSE SCIENCES END OF STUDY MEMORY With A View To Obtaining The Master's Degree In Geology Option: Petroleum Geologyen_US
dc.description.abstractThe note discusses the use of artificial intelligence in predicting porosity using the Random Forest mechanism. Challenges to traditional porosity predictions and the importance of applying artificial intelligence in this context are reviewed. Then a detailed explanation of the Random Forest mechanism and how to use it in predicting porosity was presented. Previous studies that demonstrated the effectiveness of using the Random Forest mechanism in improving the accuracy of porosity prediction are discussed. However, the use of AI in porosity prediction faces challenges such as obtaining high-quality training data and noise processing. It is emphasized that the use of artificial intelligence in porosity prediction represents a huge development in the scientific and engineering fields. This technique can improve our understanding of rock properties and contribute to improving our ability to predict porosity with greater accuracy. It can also promote resource exploration and extraction, achieve cost savings and reduce environmental impact.en_US
dc.language.isoenen_US
dc.subjectBerkine Basinen_US
dc.subjectMenzel Ladjmeten_US
dc.titlePorosity Prediction Using An Artificial Intelligence Random Forest Mechanism At The Menzel Ladjmet Field - Berkine Basin -en_US
dc.typeThesisen_US
Appears in Collections:Département des Sciences de la terre et de l’Univers - Master

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