Application of Machine Learning Algorithms for Predicting Formation Pore Pressure: A Case Study in the Sapphire field, Offshore Nile Delta, Egypt

References

1. Abdelhay, M., Hilal, A., ElessawyA. and Lala, A. [2025]. A novel approach to predict 3D pressure cubes, Sapphire Field, offshore Nile Delta, Egypt. First Break, 43(1), 43–49.
   [Google Scholar]
2. Bai, Y. and Tan, M. [2021]. Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wire line logs. Comput. Geosciences, 146, 104626.
   [Google Scholar]
3. Chen, G., Chen, L. and Li, Q. [2020]. Comparison and application of neural networks in LWD lithology identification. IOP Conf. Ser. Earth Environ. Sci., 526(1), 012146.
   [Google Scholar]
4. Dornan, T., O'Sullivan, G., O'Riain, N., Stueeken, E. and Goodhue, R. [2020]. The application of machine learning methods to aggregate geochemistry predicts quarry source location: An example from Ireland. Comput. Geosciences, 140, 104495.
   [Google Scholar]
5. Grana, D., Azevedo, L. and Liu, M. [2020]. A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data. Geophysics, 85(4), WA41–WA52.
   [Google Scholar]
6. Hidayat, F. and Astsauri, TMS. [2021]. Applied random forest for parameter sensitivity of low salinity water injection (LSWI) implementation on carbonate reservoir. Alex. Eng. J.
   [Google Scholar]
7. Hu, L., Deng, J., Zhu, H., Lin, H., Chen, Z., Deng, F.C. and Yan, C. [2013]. A new pore pressure prediction method — Back propagation artificial neural network. Elect. J. Geotech. Engn., 18, 4093–4107.
   [Google Scholar]
8. JasonB. [2021]. Machinelearningmastery.com/xgboost-for-regression.
9. Larestani, A., Mousavi, S.P., Hadavimoghaddam, F. and Hemmati-Sara-pardeh, A., [2022]. Predicting formation damage of oil fields due to mineral scaling during water-flooding operations: Gradient boosting decision tree and cascade forward back-propagation network. J. Pet. Sci. Eng., 208, 109315.
   [Google Scholar]
10. Law, B.E. and Spencer, C.W. [1998]. Abnormal pressure in hydrocarbon environments. In Law, B.E., Ulmishek, G.F., and Slavin, V.I., eds., Abnormal pressures in hydrocarbon environments. AAPG Memoir, 70, Tulsa, p. 1–11.
    [Google Scholar]
11. Lorena, A.C. and de Carvalho, A.C. [2007]. Protein cellular localization prediction with support vector machines and decision trees. Comput. Biol. Med., 37(2), 115–125.
    [Google Scholar]
12. Rio, D., Sprovieri, R. and Thunell, R. [1991]. Pliocene-lower Pleistocene Chrono-stratigraphy: a re-evaluation of Mediterranean type sections. Geol. Soc. Am. Bull., 103, 1049–1058.
    [Google Scholar]
13. Sayers, C.M., Johnson, G.M. and Denyer, G. [2002]. Predrill pore-pressure prediction using seismic data. Geophysics, 67, 1286–1292.
    [Google Scholar]
14. Singh, S.K., Tandon, R., Naithani, A.C. and Mishra, P. [2019]. Artificial intelligence in oil and gas exploration: A powerful tool for prediction, View project Reservoir characterization using deep learning View project Artificial Intelligent. In COER international conference on artificial intelligence and application, p.7.
    [Google Scholar]
15. Song, S., Hou, J., Dou, L., Song, Z. and Sun, S. [2020]. Geologist-level wire line log shape identification with recurrent neural networks. Comput. Geosciences, 134, 104313.
    [Google Scholar]
16. Taye, M.M. [2023]. Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91.
    [Google Scholar]