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European Journal of Environmental and Civil Engineering Volume 17, 2013 - Issue 8

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Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

R. Keshavarzi Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

R. Jahanbakhshi Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

Pages 675-686 | Received 29 Apr 2013, Accepted 01 Jun 2013, Published online: 11 Jul 2013

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https://doi.org/10.1080/19648189.2013.811614

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Xin Zhang, Yun-Hu Lu, Yan Jin, Mian Chen & Bo Zhou. (2023) An adaptive physics-informed deep learning method for pore pressure prediction using seismic data. Petroleum Science.
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Mingsheng Yang, Yuanbiao Hu, Baolin Liu, Lu Wang, Zheng Zhou & Mingrang Jia. (2023) Application of Artificial Neural Networks for Identification of Lithofacies by Processing of Core Drilling Data. Applied Sciences 13:21, pages 11934.
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Huayang Li, Qiang Tan, Jingen Deng, Baohong Dong, Bojia Li, Jinlong Guo, Shuiliang Zhang & Weizheng Bai. (2023) A Comprehensive Prediction Method for Pore Pressure in Abnormally High-Pressure Blocks Based on Machine Learning. Processes 11:9, pages 2603.
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Goutami Das & Saumen Maiti. (2023) A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372, New Zealand. Energy Geoscience, pages 100227.
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Melckzedeck M. Mgimba, Shu Jiang, Edwin E. Nyakilla & Grant Charles Mwakipunda. (2023) Application of GMDH to Predict Pore Pressure from Well Logs Data: A Case Study from Southeast Sichuan Basin, China. Natural Resources Research 32:4, pages 1711-1731.
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Joshua Pwavodi, Ibekwe N. Kelechi, Perekebina Angalabiri, Sharon Chioma Emeremgini & Vivian O. Oguadinma. (2023) Pore pressure prediction in offshore Niger delta using data-driven approach: Implications on drilling and reservoir quality. Energy Geoscience 4:3, pages 100194.
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Mampi Karmakar & Saumen Maiti. (2023) Statistical machine learning augmented interpretation of pore pressure of well 1344A located at slope setting of sites IODP 323. Journal of Earth System Science 132:3.
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Guozhong Gao, Omid Hazbeh, Meysam Rajabi, Somayeh Tabasi, Hamzeh Ghorbani, Reza Seyedkamali, Milad Shayanmanesh, Ahmed E. Radwan & Amir H. Mosavi. (2023) Application of GMDH model to predict pore pressure. Frontiers in Earth Science 10.
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Ahmed Abdelaal, Salaheldin Elkatatny & Abdulazeez Abdulraheem. (2022) Real-time prediction of formation pressure gradient while drilling. Scientific Reports 12:1.
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Farshad Jafarizadeh, Meysam Rajabi, Somayeh Tabasi, Reza Seyedkamali, Shadfar Davoodi, Hamzeh Ghorbani, Mehdi Ahmadi Alvar, Ahmed E. Radwan & Mako Csaba. (2022) Data driven models to predict pore pressure using drilling and petrophysical data. Energy Reports 8, pages 6551-6562.
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Honglin Huang, Jun Li, Hongwei Yang, Biao Wang, Reyu Gao, Ming Luo, Wentuo Li, Geng Zhang & Liu Liu. (2022) Research on prediction methods of formation pore pressure based on machine learning. Energy Science & Engineering 10:6, pages 1886-1901.
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Morteza Matinkia, Ali Amraeiniya, Mohammad Mohammadi Behboud, Mohammad Mehrad, Mahdi Bajolvand, Mohammad Hossein Gandomgoun & Mehdi Gandomgoun. (2022) A novel approach to pore pressure modeling based on conventional well logs using convolutional neural network. Journal of Petroleum Science and Engineering 211, pages 110156.
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Xi Chen, Weihua Cao, Chao Gan & Min Wu. (2022) A hybrid spatial model based on identified conditions for 3D pore pressure estimation. Journal of Natural Gas Science and Engineering 100, pages 104448.
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Parth Solanki, Dhruv Baldaniya, Dhruvikkumar Jogani, Bhavesh Chaudhary, Manan Shah & Ameya Kshirsagar. (2022) Artificial intelligence: New age of transformation in petroleum upstream. Petroleum Research 7:1, pages 106-114.
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Xi Chen, Weihua Cao, Chao Gan & Min Wu. (2022) A hybrid partial least squares regression-based real time pore pressure estimation method for complex geological drilling process. Journal of Petroleum Science and Engineering 210, pages 109771.
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Mohammad Farsi, Nima Mohamadian, Hamzeh Ghorbani, David A. Wood, Shadfar Davoodi, Jamshid Moghadasi & Mehdi Ahmadi Alvar. (2021) Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms. Natural Resources Research 30:5, pages 3455-3481.
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Ahmed Abdelaal, Salaheldin Elkatatny & Abdulazeez Abdulraheem. (2021) Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters. ACS Omega 6:21, pages 13807-13816.
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Abdulmalek Ahmed, Salaheldin Elkatatny, Abdulwahab Ali & Abdulazeez Abdulraheem. (2019) Comparative analysis of artificial intelligence techniques for formation pressure prediction while drilling. Arabian Journal of Geosciences 12:18.
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Abdulmalek Ahmed, Salaheldin Elkatatny, Abdulwahab Ali, Mohamed Mahmoud & Abdulazeez Abdulraheem. (2018) New Model for Pore Pressure Prediction While Drilling Using Artificial Neural Networks. Arabian Journal for Science and Engineering 44:6, pages 6079-6088.
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Mampi Karmakar & Saumen Maiti. (2019) Short term memory efficient pore pressure prediction via Bayesian neural networks at Bering Sea slope of IODP expedition 323. Measurement 135, pages 852-868.
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Okorie E. Agwu, Julius U. Akpabio, Sunday B. Alabi & Adewale Dosunmu. (2018) Artificial intelligence techniques and their applications in drilling fluid engineering: A review. Journal of Petroleum Science and Engineering 167, pages 300-315.
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Real-time prediction of pore pressure gradient through an artificial intelligence approach: a case study from one of middle east oil fields

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