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DTSTART;VALUE=DATE:20251207
DTEND;VALUE=DATE:20251212
DTSTAMP:20260423T191257
CREATED:20240808T103408Z
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UID:2642-1765065600-1765497599@www.erexegypt.com
SUMMARY:Machine Learning Integration With Seismic attributes and Inversion
DESCRIPTION:Course & Workshop\nDecember 7-11\, 2025 \nInstructor : Dr. A. Ismail\, Ph.D. \n  \nWHO SHOULD ATTEND\nThe course is designed for exploration geologists\, geophysicists\, reservoir engineers\, and development geologists. \nCourse Outlines\nThe course provides attendees with detailed knowledge of Artificial Intelligent (AI) in Exploration Geology with Seismic Application. \nCourse Content: \n1.	Introduction \n\nWhat are big data\, data analytics\, and machine learning?\nHistory of ML.\nTypes of data analytics\nGeoscience database (Numerical and Non-numerical data types).\nScales\, resolutions\, and Integration of common geologic data.\n2.	A brief review of statistical measures. \n\nRandom variable.\nCommon types of geologic data analysis.\n\nUnivariate analysis\nBivariate analysis\nTime series analysis\nSpatial analysis\nMultivariate analysis.\n\n\n3.	Basic steps in ML-based modeling \n\nIdentification of the problem.\nLearning Approaches.\n\nSupervised learning\nUnsupervised learning\nSemi-supervised learning.\nReinforcement Learning.\n\n\n4.	Data Pre-processing \n5.	Data labeling and ML-based modeling \n\nData splitting and model training.\nModel Validation and testing.\nModel evaluation.\n\n6.	A brief review of popular ML algorithms in geosciences. \n\nK-means clustering.\nRegression (linear and logistic) (K-Nearest Neighbor (KNN)).\nTerminologies used in Regression and Classification problems.\nPrincipal Component Analysis (PCA).\nEnsemble classification models (Support vector machine (SVM)).\nDecision tree.\nRandom forest.\nConvolutional neural network.\nArtificial neural networks.\n\nShallow ANN.\nDeep ANN.\n\n7.	Applications of ML in subsurface geosciences (Examples and case study) \n\nOutlier detection.\nPetrophysical log analysis.\nFracture classification.\nSeismic data analysis\nUse of seismic attributes in ML applications.\nUse of seismic inversion in ML applications.\nML for seismic facies clustering and classification.\nFault classification.\nSeismic-based rock property prediction.\nMachine learning tools and software.\n\n  \nLOCATION\nFirst day will be held at the Holiday Inn Maadi Hotel\, in Cairo. The participants will fly the next day to Hurghada. The course will be continued in Hurghada. \n  \nCOURSE FEES\nInclusive of refreshment and lunch at the Holiday Inn Maadi Hotel. Air Ticket Cairo/Hurghada return and accommodation in Hurghada. \n  \nINSTRUCTOR PROFILE\nDR. A. ISMAIL has obtained his Ph.D. in 2020 from Helwan University.  He is EREX Consultant for Seismic Interpretation and Modeling. His work involved using Neural Network Technique and Seismic Attributes for prospect identification. He is a Faculty advisor and supervisor of the AAPG (American Association of Petroleum Geologists) and has a mission to the United States for one year. He published tens of papers in reputable magazines and societies.\nRECENT CONFERENCE ABSTRACTS: \n1.  Gammaldi\, S.\, Ismail\, A. and Zollo\, A.\, 2022. The updated multi-2D image of the gas accumulation zone inferred by seismic attributes and AVO analysis at the Solfatara Volcano\, Italy (No. EGU22-11885). Copernicus Meetings.\n2.   Khalil\, A.\, Nawawy\, M.\, Ismail\, A.\, 2021. Shallow Offshore Seabottom Geotechnical Modeling Using One Channel Acoustic Streamer at Kuala Sanglang\, Perlis\, Peninsular Malaysia. In The Arab Conference on Astronomy and Geophysics (ID. 197).\n3.   Gammaldi\, S.\, Ismail\, A.\, Chiuso\, T. and Zollo\, A.\, 2020\, May. The multi-2D seismic imaging of the Solfatara Volcano\, Italy\, inferred by seismic attributes. In EGU General Assembly Conference Abstracts (p. 16478).\n4.   Ismail\, A.\, Ewida\, H. F.\, Al-Ibiary\, M. G.\, Gammaldi\, S.\, & Zollo\, A.\, 2019. Neural network technique and seismic attributes\, west offshore Nile Delta\, Egypt. Petroleum Geology Student Contest – 3rd edition\, Calvello\, Italy 2019. doi: 10.3301/ABSGI.2019.06
URL:https://www.erexegypt.com/wp/short-course/artificial-intelligent-ai-in-exploration-geology-and-seismic-application/
LOCATION:Hurghada\, Hurghada\, Egypt
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