View source for Utilization of the industrial revolution 4.0 in oil and gas exploration

====Structure Interpretation and Facies Detection====
 
Characterizing the fracture of reservoir formations is important in determining the flow of oil and gas and eventually the productivity of wells. Therefore, geoscientists delineate the fracture connectivity, orientation and location. El Ouahed et al.<ref name=Elquahedetal_2005>1.	El Ouahed, A. K., Tiab, D., & Mazouzi, A. (2005). Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria. Journal of Petroleum Science and Engineering, 49(3–4), 122–141. https://doi.org/10.1016/j.petrol.2005.05.003 </ref> applied Artificial Neural Network and Fuzzy Logic to build a 2D fracture intensity map of an oil field in Algeria. Many case studies have emerged from the application of artificial intelligence for structure interpretation. For instance, Obafemi et al.<ref name=Obafemietal_2020>Obafemi, S., Oyedele, K. F., Omeru, T., & Bankole, S. I. (2020). Characterization of deep water turbidite channels and submarine fan lobes using artificial intelligence; Case study of Frem Field deep offshore Niger Delta. Journal of African Earth Sciences, 167(April), 103852. https://doi.org/10.1016/j.jafrearsci.2020.103852</ref> have shown the use of Unsupervised Artificial Neural Network (UANN) in characterizing reservoirs in Deep-water Niger Delta. In addition, from 3D seismic data, one of the main attributes is to extract the seismic facies and this is used to infer about the geological depositional environment, which is mainly used for reservoir characterization. Liu et al.<ref name=Liuetal_2020>Liu, B., Li, X., Zheng, G., Ren, Y., Zhang, S., Liu, Y., Gao, L., Liu, Y., Zhang, B., & Wang, F. (2020). Deep-learning-based information mining from ocean remote-sensing imagery. National Science Review, 7(10), 1584–1605. https://doi.org/10.1093/nsr/nwaa047</ref> demonstrated seismic facies classification using supervised convolutional neural network (CNN) and a semi-supervised generative adversarial network (GAN). The CNN method is a deep learning algorithm that can take an input image and classify it based on its features (edges, corners, etc) used in mature fields where the well data are immense, whereas the GAN is similar to CNN but used when the well data is limited in new prospects (new fields).<ref name=Liuetal_2020 /> <ref name=Dramsch_2020>Dramsch, J. S. (2020). 70 Years of Machine Learning in Geoscience in Review. Advances in Geophysics, 61(January), 1–55. https://doi.org/10.1016/bs.agph.2020.08.002</ref> <ref name=Bergenetal_2019>Bergen, K. J., Johnson, P. A., De Hoop, M. V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363(6433). https://doi.org/10.1126/science.aau0323</ref> <ref name=Tontiwachwuthikuletal_2020>Tontiwachwuthikul, P. (PT), Chan, C. W., Zeng, F. (Bill), Liang, Z. (Henry), Sema, T., & Chao, M. (2020). Recent progress and new developments of applications of artificial intelligence (AI), knowledge-based systems (KBS), and Machine Learning (ML) in the petroleum industry. Petroleum, xxxx, 0–1. https://doi.org/10.1016/j.petlm.2020.08.001</ref> <ref name=Dietal_2019>Di, H., Li, C., Smith, S., & Abubakar, A. (2019). Machine learning-assisted seismic interpretation with geologic constraints. SEG International Exposition and Annual Meeting 2019, 5360–5364. https://doi.org/10.1190/segam2019-w4-01.1</ref> In addition, Wang et al.<ref name=Wangetal_2020>Wang, Z., Gao, D., Lei, X., Wang, D., & Gao, J. (2020). Machine learning-based seismic spectral attribute analysis to delineate a tight-sand reservoir in the Sulige gas field of central Ordos Basin, western China. Marine and Petroleum Geology, 113(October 2019), 104136. https://doi.org/10.1016/j.marpetgeo.2019.104136</ref> and <ref name=Zhang et al_2018>Zhang, G., Wang, Z., Li, H., Sun, Y., Zhang, Q., & Chen, W. (2018). Permeability prediction of isolated channel sands using machine learning. Journal of Applied Geophysics, 159, 605–615. https://doi.org/10.1016/j.jappgeo.2018.09.011</ref> utilized ML to delineate and predict permeability of tight-sand (unconventional) reservoirs in China, their study has demonstrated its usefulness in the exploration phase and well placement.