{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T03:35:41Z","timestamp":1782790541744,"version":"3.54.5"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100009749","name":"Robert Gordon University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009749","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Industrial Engineering"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.cie.2026.111970","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:33:20Z","timestamp":1774283600000},"page":"111970","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["ML-based soft sensing and decision-making for data-driven formation pressure prediction"],"prefix":"10.1016","volume":"216","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0987-2791","authenticated-orcid":false,"given":"Andrei","family":"Petrovski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murshedul","family":"Arifeen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Kotenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maksim","family":"Sletov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phil","family":"Hassard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junayad","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cie.2026.111970_b1","series-title":"On the rig: OTR","author":"3t drilling systems","year":"2024"},{"key":"10.1016\/j.cie.2026.111970_b2","doi-asserted-by":"crossref","first-page":"13807","DOI":"10.1021\/acsomega.1c01340","article-title":"Data-driven modelling approach for pressure gradient prediction while drilling from drilling parameters","volume":"6","author":"Abdelaal","year":"2021","journal-title":"ACS Omega"},{"issue":"21","key":"10.1016\/j.cie.2026.111970_b3","doi-asserted-by":"crossref","first-page":"13807","DOI":"10.1021\/acsomega.1c01340","article-title":"Data-driven modeling approach for pore pressure gradient prediction while drilling from drilling parameters","volume":"6","author":"Abdelaal","year":"2021","journal-title":"ACS Omega"},{"issue":"1","key":"10.1016\/j.cie.2026.111970_b4","doi-asserted-by":"crossref","first-page":"11318","DOI":"10.1038\/s41598-022-15493-z","article-title":"Real-time prediction of formation pressure gradient while drilling","volume":"12","author":"Abdelaal","year":"2022","journal-title":"Scientific Reports"},{"issue":"6","key":"10.1016\/j.cie.2026.111970_b5","doi-asserted-by":"crossref","first-page":"6079","DOI":"10.1007\/s13369-018-3574-7","article-title":"New model for pore pressure prediction while drilling using artificial neural networks","volume":"44","author":"Ahmed","year":"2019","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"7","key":"10.1016\/j.cie.2026.111970_b6","doi-asserted-by":"crossref","DOI":"10.3390\/pr12071365","article-title":"Toward enhanced efficiency: Soft sensing and intelligent modeling in industrial electrical systems","volume":"12","author":"Ar\u00e9valo","year":"2024","journal-title":"Processes"},{"key":"10.1016\/j.cie.2026.111970_b7","series-title":"Comprehensive dataset for predicting formation pressure and detecting kicks in offshore drilling rigs","author":"Arifeen","year":"2024"},{"key":"10.1016\/j.cie.2026.111970_b8","series-title":"DataDRILL: Predicting formation pressure and detecting kicks in drilling rigs","author":"Arifeen","year":"2024"},{"issue":"1","key":"10.1016\/j.cie.2026.111970_b9","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.petsci.2024.12.014","article-title":"Real-time drilling torque prediction ahead of the bit with just-in-time learning","volume":"22","author":"Bai","year":"2025","journal-title":"Petroleum Science"},{"key":"10.1016\/j.cie.2026.111970_b10","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1016\/j.procir.2020.01.082","article-title":"Industrial case studies for digital transformation of engineering processes using the virtual reality technology","volume":"90","author":"Bellalouna","year":"2020","journal-title":"Procedia CIRP"},{"issue":"1","key":"10.1016\/j.cie.2026.111970_b11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.cie.2026.111970_b12","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1016\/j.procs.2024.06.430","article-title":"Artificial intelligence models: A literature review addressing industry 4.0 approach","volume":"239","author":"Castro","year":"2024","journal-title":"Procedia Computer Science"},{"issue":"1","key":"10.1016\/j.cie.2026.111970_b13","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s00603-022-03089-y","article-title":"Pore pressure prediction by empirical and machine learning methods using conventional and drilling logs in carbonate rocks","volume":"56","author":"Delavar","year":"2023","journal-title":"Rock Mechanics and Rock Engineering"},{"key":"10.1016\/j.cie.2026.111970_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.jii.2024.100734","article-title":"A machine learning and fuzzy logic model for optimizing digital transformation in renewable energy: Insights into industrial information integration","volume":"42","author":"Eti","year":"2024","journal-title":"Journal of Industrial Information Integration"},{"key":"10.1016\/j.cie.2026.111970_b15","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1007\/s11053-021-09852-2","article-title":"Predicting formation pore-pressure from well-log data with hybrid machine-learning optimization algorithms","volume":"30","author":"Farsi","year":"2021","journal-title":"Natural Resources Research"},{"issue":"2","key":"10.1016\/j.cie.2026.111970_b16","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/S1876-3804(23)60396-9","article-title":"Rock physics model for velocity\u2014pressure relations and its application to shale pore pressure estimation","volume":"50","author":"Guo","year":"2023","journal-title":"Petroleum Exploration and Development"},{"key":"10.1016\/j.cie.2026.111970_b17","doi-asserted-by":"crossref","DOI":"10.3390\/s21196340","article-title":"A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics","volume":"21","author":"Huang","year":"2021","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"11","key":"10.1016\/j.cie.2026.111970_b18","doi-asserted-by":"crossref","first-page":"12868","DOI":"10.1109\/JSEN.2020.3033153","article-title":"A review on soft sensors for monitoring, control, and optimization of industrial processes","volume":"21","author":"Jiang","year":"2020","journal-title":"IEEE Sensors Journal"},{"key":"10.1016\/j.cie.2026.111970_b19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2021.10.005","article-title":"Social physics","volume":"948","author":"Jusup","year":"2022","journal-title":"Physics Reports"},{"issue":"4","key":"10.1016\/j.cie.2026.111970_b20","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.compchemeng.2008.12.012","article-title":"Data-driven soft sensors in the process industry","volume":"33","author":"Kadlec","year":"2009","journal-title":"Computers & Chemical Engineering"},{"issue":"1","key":"10.1016\/j.cie.2026.111970_b21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compchemeng.2010.07.034","article-title":"Review of adaptation mechanisms for data-driven soft sensors","volume":"35","author":"Kadlec","year":"2011","journal-title":"Computers & Chemical Engineering"},{"issue":"36","key":"10.1016\/j.cie.2026.111970_b22","doi-asserted-by":"crossref","first-page":"31691","DOI":"10.1021\/acsomega.2c01602","article-title":"New models for predicting pore pressure and fracture pressure while drilling in mixed lithologies using artificial neural networks","volume":"7","author":"Khaled","year":"2022","journal-title":"ACS Omega"},{"issue":"5","key":"10.1016\/j.cie.2026.111970_b23","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1007\/s12517-023-11373-6","article-title":"ANN-based estimation of pore pressure of hydrocarbon reservoirs\u2014a case study","volume":"16","author":"Kianoush","year":"2023","journal-title":"Arabian Journal of Geosciences"},{"issue":"11","key":"10.1016\/j.cie.2026.111970_b24","doi-asserted-by":"crossref","first-page":"6805","DOI":"10.1007\/s00603-022-02992-8","article-title":"Correlating the unconfined compressive strength of rock with the compressional wave velocity effective porosity and Schmidt Hammer rebound number using artificial neural networks","volume":"55","author":"Le","year":"2022","journal-title":"Rock Mechanics and Rock Engineering"},{"issue":"02","key":"10.1016\/j.cie.2026.111970_b25","doi-asserted-by":"crossref","first-page":"98","DOI":"10.2118\/144717-JPT","article-title":"Pore-pressure and wellbore-stability prediction to increase drilling efficiency","volume":"64","author":"Li","year":"2012","journal-title":"Journal of Petroleum Technology"},{"key":"10.1016\/j.cie.2026.111970_b26","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.eng.2022.07.014","article-title":"Intelligent drilling and completion: A review","volume":"18","author":"Li","year":"2022","journal-title":"Engineering"},{"key":"10.1016\/j.cie.2026.111970_b27","article-title":"Spatial distribution prediction of pore pressure based on mamba model","volume":"Volume 13 - 2025","author":"Liu","year":"2025","journal-title":"Frontiers in Earth Science"},{"key":"10.1016\/j.cie.2026.111970_b28","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isatra.2020.03.011","article-title":"Autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development","volume":"103","author":"Liu","year":"2020","journal-title":"ISA Transactions"},{"issue":"1016","key":"10.1016\/j.cie.2026.111970_b29","article-title":"Modeling flexural and compressive strengths behaviour of cement-grouted sands modified with water reducer polymer","volume":"12","author":"Mahmood","year":"2022","journal-title":"Applied Sciences"},{"issue":"19","key":"10.1016\/j.cie.2026.111970_b30","article-title":"Interpreting the experimental results of compressive strength of hand-mixed cement-grouted sands using various mathematical approaches","volume":"22","author":"Mahmood","year":"2021","journal-title":"Archives of Civil and Mechanical Engineering"},{"key":"10.1016\/j.cie.2026.111970_b31","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.1016\/j.procs.2024.02.020","article-title":"Investigating the accuracy of artificial neural network models in predicting surface roughness in drilling processes","volume":"232","author":"Okwu","year":"2024","journal-title":"Procedia Computer Science"},{"key":"10.1016\/j.cie.2026.111970_b32","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.psep.2020.09.038","article-title":"Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations","volume":"147","author":"Osarogiagbon","year":"2021","journal-title":"Process Safety and Environmental Protection"},{"issue":"2","key":"10.1016\/j.cie.2026.111970_b33","first-page":"503","article-title":"Human complementation must aid automation to mitigate unemployment effects due to AI technologies in the labor market","volume":"5","author":"\u00d6zer","year":"2024","journal-title":"Refektif Journal of Social Sciences"},{"key":"10.1016\/j.cie.2026.111970_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111095","article-title":"Artificial intelligence-based predictive models for shear wave velocity of soils: A comprehensive review","volume":"155","author":"Payan","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.cie.2026.111970_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105988","article-title":"The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: A critical review","volume":"121","author":"Perera","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.cie.2026.111970_b36","series-title":"Proceedings of the ninth international scientific conference \u201cintelligent information technologies for industry\u201d","first-page":"14","article-title":"Machine learning-based intelligent measurement in industrial digital twins","volume":"vol. 2","author":"Petrovski","year":"2026"},{"key":"10.1016\/j.cie.2026.111970_b37","article-title":"Insider threat detection within operational technology using digital twins","volume":"vol. 1","author":"Petrovski","year":"2024"},{"key":"10.1016\/j.cie.2026.111970_b38","series-title":"Proceedings of the eighth international scientific conference on intelligent information technologies for industry","first-page":"25","article-title":"Insider threat detection within operational technology using digital twins","volume":"vol. 2","author":"Petrovski","year":"2024"},{"issue":"2","key":"10.1016\/j.cie.2026.111970_b39","doi-asserted-by":"crossref","first-page":"11768","DOI":"10.1016\/j.ifacol.2023.10.565","article-title":"Advanced soft-sensor systems for process monitoring, control, optimisation, and fault diagnosis","volume":"56","author":"Shardt","year":"2023","journal-title":"IFAC-PapersOnLine"},{"issue":"4","key":"10.1016\/j.cie.2026.111970_b40","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.ptlrs.2021.05.009","article-title":"Application of machine learning and artificial intelligence in oil and gas industry","volume":"6","author":"Sircar","year":"2021","journal-title":"Petroleum Research"},{"key":"10.1016\/j.cie.2026.111970_b41","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Statistics and Computing"},{"key":"10.1016\/j.cie.2026.111970_b42","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.chemolab.2015.12.011","article-title":"Review of soft sensor methods for regression applications","volume":"152","author":"Souza","year":"2016","journal-title":"Chemometrics and Intelligent Laboratory Systems"},{"key":"10.1016\/j.cie.2026.111970_b43","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1007\/s00180-023-01382-0","article-title":"Hermiter: R package for sequential nonparametric estimation","volume":"39","author":"Stephanou","year":"2024","journal-title":"Computational Statatistics"},{"issue":"9","key":"10.1016\/j.cie.2026.111970_b44","doi-asserted-by":"crossref","first-page":"5853","DOI":"10.1109\/TII.2021.3053128","article-title":"A survey on deep learning for data-driven soft sensors","volume":"17","author":"Sun","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.1016\/j.cie.2026.111970_b45","doi-asserted-by":"crossref","DOI":"10.1016\/j.marpolbul.2026.119398","article-title":"The reliability gap: Why high predictive accuracy doesn\u2019t guarantee stable feature importance","volume":"226","author":"Takefuji","year":"2026","journal-title":"Marine Pollution Bulletin"},{"key":"10.1016\/j.cie.2026.111970_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.geoen.2024.212995","article-title":"Automated classification of drill string vibrations using machine learning algorithms","volume":"239","author":"Wang","year":"2024","journal-title":"Geoenergy Science and Engineering"},{"key":"10.1016\/j.cie.2026.111970_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.geoen.2024.212747","article-title":"Prediction method for formation pore pressure based on transfer learning","volume":"236","author":"Xu","year":"2024","journal-title":"Geoenergy Science and Engineering"},{"key":"10.1016\/j.cie.2026.111970_b48","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.neucom.2018.11.107","article-title":"Deep quality-related feature extraction for soft sensing modeling: A deep learning approach with hybrid VW-SAE","volume":"396","author":"Yuan","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.cie.2026.111970_b49","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ins.2020.03.018","article-title":"Stacked isomorphic autoencoder based soft analyzer and its application to sulfur recovery unit","volume":"534","author":"Yuan","year":"2020","journal-title":"Information Sciences"},{"issue":"1","key":"10.1016\/j.cie.2026.111970_b50","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.earscirev.2011.06.001","article-title":"Pore pressure prediction from well logs: Methods, modifications, and new approaches","volume":"108","author":"Zhang","year":"2011","journal-title":"Earth-Science Reviews"},{"issue":"5","key":"10.1016\/j.cie.2026.111970_b51","doi-asserted-by":"crossref","first-page":"3847","DOI":"10.1007\/s00366-020-01267-6","article-title":"Improved levenberg\u2013marquardt backpropagation neural network by particle swarm and whale optimization algorithms to predict the deflection of RC beams","volume":"38","author":"Zhao","year":"2022","journal-title":"Engineering with Computers"}],"container-title":["Computers & Industrial Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360835226001713?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0360835226001713?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T03:35:32Z","timestamp":1777952132000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0360835226001713"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":51,"alternative-id":["S0360835226001713"],"URL":"https:\/\/doi.org\/10.1016\/j.cie.2026.111970","relation":{},"ISSN":["0360-8352"],"issn-type":[{"value":"0360-8352","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ML-based soft sensing and decision-making for data-driven formation pressure prediction","name":"articletitle","label":"Article Title"},{"value":"Computers & Industrial Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cie.2026.111970","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111970"}}