Abdel-Fattah, M. I., Metwalli, F. I., & El Sayed, I. M. (2018). Static reservoir modeling of the Bahariya reservoirs for the oilfields development in South Umbarka area, Western Desert, Egypt. Journal of African Earth Sciences, 138, 1-13.
Adelu, A. O., Aderemi, A. A., Akanji, A. O., Sanuade, O. A., Kaka, S. I., Afolabi, O., .Olugbemiga, S., & Oke, R. (2019). Application of 3D static modeling for optimal reservoir characterization. Journal of African Earth Sciences, 152, 184-196.
Agwu, O. E., Okoro, E. E., & Sanni, S. E. (2022). Modelling oil and gas flow rate through chokes: A critical review of extant models. Journal of Petroleum Science and Engineering, 208, 109775.
Asghar, S., Choi, J., Yoon, D., & Byun, J. (2020). Spatial pseudo-labeling for semi-supervised facies classification. Journal of Petroleum Science and Engineering, 195, 107834.
Besag, J., Green, P.J., 1993. Spatial statistics and Bayesian computation. Journal of the Royal Statistical Society: Series B (Methodological), 55(1), pp.25-37.
Bornard, R., Allo, F., Coléou, T., Freudenreich, Y., Caldwell, D. H., & Hamman, J. G. (2005). Petrophysical Seismic Inversion to Determine More Accurate and Precise Reservoir Properties (SPE94144). In 67th EAGE Conference & Exhibition.
Caers, J. and Hoffman, T., 2006. The probability perturbation method: a new look at Bayesian inverse modeling. Mathematical geology, 38(1), pp.81-100.
Castro, S. A., Caers, J., Otterlei, C., Meisingset, H., Hoye, T., Gomel, P., & Zachariassen, E. (2009). Incorporating 4D seismic data into reservoir models while honoring production and geologic data: A case study. The Leading Edge, 28 (12), 1498-1505.
Celma, R. I., Singh, N., Ouldamer, K., & Debec, P. (2021, December). Petroelastic Model PEM for a Highly Heterogeneous Cretaceous Reservoir in Middle East. In Abu Dhabi International Petroleum Exhibition & Conference. OnePetro.
Del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. G. (2008). Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Transactions on evolutionary computation, 12 (2), 171-195.
Ding, S., Lu, R., Xi, Y., Liu, G., & Ma, J. (2020). Efficient well placement optimization coupling hybrid objective function with particle swarm optimization algorithm. Applied Soft Computing, 95, 106511.
Doyen, P. (2007). Seismic reservoir characterization: An earth modelling perspective, Vol. 2, p. 255. Houten: EAGE publications.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 39-43. IEEE.
Eberhart, R. C., & Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization. In International conference on evolutionary programming, 611-616. Springer, Berlin, Heidelberg.
Emami Niri, M., & Lumley, D. E. (2015). Simultaneous optimization of multiple objective functions for reservoir modeling. Geophysics, 80 (5), M53-M67.
Gao, J., Zheng, Y., Ni, K., Zhang, H., Hao, B., & Yan, J. (2021, November). Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine. In Journal of Physics: Conference Series (Vol. 2076, No. 1, p. 012004). IOP Publishing.
Grana, D., Mukerji, T., Dvorkin, J. and Mavko, G. (2012). Stochastic inversion of facies from seismic
data based on sequential simulations and probability perturbation method. Geophysics, 77(4): M53-
M72.
Gusev, S. I. (2020). Petro-elastic modeling deliverables for the kharyaga permian carbonate deposits. Georesursy, 22(3), 62-68.
Hoffman, B.T. and Caers, J., 2003, January. Geostatistical history matching using a regional probability perturbation method. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers.
Kassem, H. S. (2019). Oil and gas production system optimization using particle swarm optimization (Doctoral dissertation).
Kozlovskaya, E., Vecsey, L., Plomerová, J., & Raita, T. (2007). Joint inversion of multiple data types with the use of multi objective optimization: problem formulation and application to the seismic anisotropy investigations. Geophysical Journal International, 171 (2), 761-779.
Kumar, A. (2021). Hybrid Evolutionary Optimization Approach for Oilfield Well Control Optimization. arXiv preprint arXiv:2103.15608.
Liu, C., Wu, L., Huang, X., & Xiao, W. (2022). Improved dynamic adaptive ant colony optimization algorithm to solve pipe routing design. Knowledge-Based Systems, 237, 107846.
Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-165.
Ojugo, A. A., & Obruche, C. O. (2022). Quest For Convergence Solution Using Hybrid Genetic Algorithm Trained Neural Network Model For Metamorphic Malware Detection. ARRUS Journal of Engineering and Technology, 2(1).
Ravalec-Dupin, L., Enchery, G., Baroni, A., & Da Veiga, S. (2011). Preselection of reservoir models from a geostatistics-based petrophysical seismic inversion. SPE Reservoir Evaluation & Engineering, 14 (05), 612-620.
Sakhautdinov, I., & Vakhitova, G. (2018). Prediction reservoirs based on the results of petro-elastic modeling. International Multidisciplinary Scientific GeoConference: SGEM, 18(1.4), 689-696.
Salman, N., Lawi, A., & Syarif, S. (2018, November). Artificial neural network backpropagation with particle swarm optimization for crude palm oil price prediction. In Journal of Physics: Conference Series (Vol. 1114, No. 1, p. 012088). IOP Publishing.
Schwarzbach, C., Börner, R. U., & Spitzer, K. (2005). Twodimensional inversion of direct current resistivity data using a parallel, multi-objective genetic algorithm. Geophysical Journal International, 162 (3), 685-695.
Semnani, A., Ostadhassan, M., Xu, Y., Sharifi, M., & Liu, B. (2021). Joint optimization of constrained well placement and control parameters using teaching-learning based optimization and an inter-distance algorithm. Journal of Petroleum Science and Engineering, 203, 108652.
Shams, M., El-Banbi, A., & Sayyouh, M. H. (2021, September). Applications of Honey Bee Optimization in Reservoir Engineering Assisted History Matching. In SPE Annual Technical Conference and Exhibition. OnePetro.
Solanki, P., Baldaniya, D., Jogani, D., Chaudhary, B., Shah, M., & Kshirsagar, A. (2021). Artificial intelligence: new age of transformation in petroleum upstream. Petroleum Research.
Su-Mei, H., Zhao-Hui, S., Meng-Ke, Z., San-Yi, Y., & Shang-Xu, W. (2022). Incremental semi-supervised learning for intelligent seismic facies identification. Applied Geophysics, 19(1), 41-52.
Okwu, M. O., & Tartibu, L. K. (2021). Introduction to Optimization. In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications (pp. 1-4). Springer, Cham.
Zhang, T., Tang, Z., Wu, J., Du, X., & Chen, K. (2021). Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm. Energy, 229, 120797.
Zhan, Z. H., Zhang, J., Li, Y., & Chung, H. S. H. (2009). Adaptive particle swarm optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39 (6), 1362-1381.
Zhou, J., Mannini, A., & Cocker, J. (2022, February). Petro-elastic Inversion case study in the Otway Basin. In Second EAGE Conference on Seismic Inversion (Vol. 2022, No. 1, pp. 1-5). European Association of Geoscientists & Engineers.
)