{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:28:28Z","timestamp":1758079708012,"version":"3.44.0"},"reference-count":71,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s00366-025-02113-3","type":"journal-article","created":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T11:37:12Z","timestamp":1740310632000},"page":"2533-2557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A robust hybrid near-real-time model for prediction of drilling fluids filtration"],"prefix":"10.1007","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1733-1677","authenticated-orcid":false,"given":"Shadfar","family":"Davoodi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1342-9212","authenticated-orcid":false,"given":"Mohammed","family":"Al-Shargabi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3202-4069","authenticated-orcid":false,"given":"David A.","family":"Wood","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3623-2474","authenticated-orcid":false,"given":"Mohammad","family":"Mehrad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7063-9756","authenticated-orcid":false,"given":"Valeriy S.","family":"Rukavishnikov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,23]]},"reference":[{"key":"2113_CR1","doi-asserted-by":"crossref","unstructured":"Deville JP (2022) Chapter 4 -Drilling fluids. In: Fluid Chemistry, Drilling and Completion. Gulf Professional Publishing, pp 115\u2013185","DOI":"10.1016\/B978-0-12-822721-3.00010-1"},{"key":"2113_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/J.PETROL.2022.110318","volume":"213","author":"S Gautam","year":"2022","unstructured":"Gautam S, Guria C, Rajak VK (2022) A state of the art review on the performance of high-pressure and high-temperature drilling fluids: Towards understanding the structure-property relationship of drilling fluid additives. J Pet Sci Eng 213:110318. https:\/\/doi.org\/10.1016\/J.PETROL.2022.110318","journal-title":"J Pet Sci Eng"},{"key":"2113_CR3","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1007\/s13202-022-01589-9","volume":"13","author":"YC Ning","year":"2023","unstructured":"Ning YC, Ridha S, Ilyas SU et al (2023) Application of machine learning to determine the shear stress and filtration loss properties of nano-based drilling fluid. J Pet Explor Prod Technol 13:1031\u20131052. https:\/\/doi.org\/10.1007\/s13202-022-01589-9","journal-title":"J Pet Explor Prod Technol"},{"key":"2113_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2020.107028","author":"A Kariman Moghaddam","year":"2020","unstructured":"Kariman Moghaddam A, Ramazani Saadatabadi A (2020) Rheological modeling of water based drilling fluids containing polymer\/bentonite using generalized bracket formalism. J Pet Sci Eng. https:\/\/doi.org\/10.1016\/j.petrol.2020.107028","journal-title":"J Pet Sci Eng"},{"key":"2113_CR5","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/J.COLSURFA.2016.07.092","volume":"507","author":"K Song","year":"2016","unstructured":"Song K, Wu Q, Li M et al (2016) Water-based bentonite drilling fluids modified by novel biopolymer for minimizing fluid loss and formation damage. Colloids Surf A Physicochem Eng Asp 507:58\u201366. https:\/\/doi.org\/10.1016\/J.COLSURFA.2016.07.092","journal-title":"Colloids Surf A Physicochem Eng Asp"},{"key":"2113_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.04.069","author":"S Davoodi","year":"2019","unstructured":"Davoodi S, Ramazani SAA, Soleimanian A, Fellah Jahromi A (2019) Application of a novel acrylamide copolymer containing highly hydrophobic comonomer as filtration control and rheology modifier additive in water-based drilling mud. J Pet Sci Eng. https:\/\/doi.org\/10.1016\/j.petrol.2019.04.069","journal-title":"J Pet Sci Eng"},{"key":"2113_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.molliq.2019.111635","author":"S Ghaderi","year":"2019","unstructured":"Ghaderi S, Ramazani SAA, Haddadi SA (2019) Applications of highly salt and highly temperature resistance terpolymer of acrylamide\/styrene\/maleic anhydride monomers as a rheological modifier: Rheological and corrosion protection properties studies. J Mol Liq. https:\/\/doi.org\/10.1016\/j.molliq.2019.111635","journal-title":"J Mol Liq"},{"key":"2113_CR8","doi-asserted-by":"crossref","unstructured":"Ezell RG, Ezzat AM, Turner JK, Wu JJ (2010) New Filtration-Control Polymer for Improved Brine-Based Reservoir Drilling-Fluids Performance at Temperatures in Excess of 400\u00b0F and High Pressure. In: Proc. SPE Int. Symp. Exhib. Form. Damage Control. Lafayette, Louisiana, USA. SPE-128119-MS, pp 25\u201332","DOI":"10.2118\/128119-MS"},{"key":"2113_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.petrol.2019.106727","author":"S Gul","year":"2020","unstructured":"Gul S, van Oort E (2020) A machine learning approach to filtrate loss determination and test automation for drilling and completion fluids. J Pet Sci Eng. https:\/\/doi.org\/10.1016\/j.petrol.2019.106727","journal-title":"J Pet Sci Eng"},{"key":"2113_CR10","unstructured":"Caenn R, Darley HCH, Gray GR (2016) Composition and Properties of Drilling and Completion Fluids: Seventh Edition. Gulf Professional Publishing"},{"key":"2113_CR11","doi-asserted-by":"publisher","first-page":"6594","DOI":"10.3390\/S23146594","volume":"23","author":"A-S M Al-Rubaii","year":"2023","unstructured":"Al-Rubaii A-S M, Aldahlawi B et al (2023) A developed robust model and artificial intelligence techniques to predict drilling fluid density and equivalent circulation density in real time. Sensors 23:6594. https:\/\/doi.org\/10.3390\/S23146594","journal-title":"Sensors"},{"key":"2113_CR12","doi-asserted-by":"crossref","unstructured":"Darley HCH, Gray GR (1988) Chapter 3 - Equipment and procedures for evaluating drilling fluid performance. In: Composition and Properties of Drilling and Completion Fluids, 5th edn. Gulf Professional Publishing, pp 91\u2013139","DOI":"10.1016\/B978-0-08-050241-0.50007-2"},{"key":"2113_CR13","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.eng.2022.06.009","volume":"18","author":"MA Mirza","year":"2022","unstructured":"Mirza MA, Ghoroori M, Chen Z (2022) Intelligent petroleum engineering. Engineering 18:27\u201332. https:\/\/doi.org\/10.1016\/j.eng.2022.06.009","journal-title":"Engineering"},{"key":"2113_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/J.COLSURFA.2024.133336","volume":"686","author":"S Deng","year":"2024","unstructured":"Deng S, Huo B, Xu S et al (2024) Prediction of water-in-oil emulsion drilling fluids rheological properties based on GPR-Bagging ensemble learning. Colloids Surf A Physicochem Eng Asp 686:133336. https:\/\/doi.org\/10.1016\/J.COLSURFA.2024.133336","journal-title":"Colloids Surf A Physicochem Eng Asp"},{"key":"2113_CR15","doi-asserted-by":"publisher","first-page":"14371","DOI":"10.1021\/ACSOMEGA.2C06656\/ASSET\/IMAGES\/LARGE\/AO2C06656_0017.JPEG","volume":"8","author":"A Abdelaal","year":"2023","unstructured":"Abdelaal A, Ibrahim AF, Elkatatny S (2023) Data-driven framework for real-time rheological properties prediction of flat rheology synthetic oil-based drilling fluids. ACS Omega 8:14371\u201314386. https:\/\/doi.org\/10.1021\/ACSOMEGA.2C06656\/ASSET\/IMAGES\/LARGE\/AO2C06656_0017.JPEG","journal-title":"ACS Omega"},{"key":"2113_CR16","doi-asserted-by":"publisher","DOI":"10.22059\/JCHPE.2024.380925.1551","author":"R Rooki","year":"2025","unstructured":"Rooki R, Rahimi M (2025) Prediction of rheological properties of drilling fluids using two artificial intelligence methods: general regression neural network and fuzzy logic. J Chem Pet Eng. https:\/\/doi.org\/10.22059\/JCHPE.2024.380925.1551","journal-title":"J Chem Pet Eng"},{"key":"2113_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/J.COLSURFA.2024.135906","volume":"707","author":"S Davoodi","year":"2025","unstructured":"Davoodi S, Burnaev E, Wood DA et al (2025) An integrated intelligent approach to the determination of drilling fluids\u2019 solid content. Colloids Surf A Physicochem Eng Asp 707:135906. https:\/\/doi.org\/10.1016\/J.COLSURFA.2024.135906","journal-title":"Colloids Surf A Physicochem Eng Asp"},{"key":"2113_CR18","doi-asserted-by":"publisher","DOI":"10.1115\/OMAE2021-63094","author":"S Gul","year":"2021","unstructured":"Gul S (2021) Machine learning applications in drilling fluid engineering: a review. Proc Int Conf Offshore Mech Arct Eng - OMAE. https:\/\/doi.org\/10.1115\/OMAE2021-63094","journal-title":"Proc Int Conf Offshore Mech Arct Eng - OMAE"},{"key":"2113_CR19","doi-asserted-by":"crossref","unstructured":"Oguntade T, Ojo T, Efajemue E, et al (2020) Application of ANN in Predicting Water Based Mud Rheology and Filtration Properties. In: SPE Niger. Annu. Int. Conf. Exhib. SPE-203720-MS","DOI":"10.2118\/203720-MS"},{"key":"2113_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106459","volume":"123","author":"S Davoodi","year":"2023","unstructured":"Davoodi S, Mehrad M, Wood DA et al (2023) Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids. Eng Appl Artif Intell 123:106459. https:\/\/doi.org\/10.1016\/j.engappai.2023.106459","journal-title":"Eng Appl Artif Intell"},{"key":"2113_CR21","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1007\/s13202-019-00776-5","volume":"10","author":"A Golsefatan","year":"2020","unstructured":"Golsefatan A, Shahbazi K (2020) A comprehensive modeling in predicting the effect of various nanoparticles on filtration volume of water-based drilling fluids. J Pet Explor Prod Technol 10:859\u2013870. https:\/\/doi.org\/10.1007\/s13202-019-00776-5","journal-title":"J Pet Explor Prod Technol"},{"key":"2113_CR22","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1080\/15567036.2019.1639854","volume":"43","author":"A Golsefatan","year":"2021","unstructured":"Golsefatan A, Shahbazi K (2021) Predicting performance of SiO2 nanoparticles on filtration volume using reliable approaches: application in water-based drilling fluids. Energy Sour, Part A Recover Util Environ Eff 43:3216\u20133225. https:\/\/doi.org\/10.1080\/15567036.2019.1639854","journal-title":"Energy Sour, Part A Recover Util Environ Eff"},{"key":"2113_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.petlm.2021.04.002","author":"A Lekomtsev","year":"2021","unstructured":"Lekomtsev A, Keykhosravi A, Moghaddam MB et al (2021) On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models. Petroleum. https:\/\/doi.org\/10.1016\/j.petlm.2021.04.002","journal-title":"Petroleum"},{"key":"2113_CR24","doi-asserted-by":"publisher","first-page":"12","DOI":"10.48048\/tis.2023.6736","volume":"20","author":"M Gasser","year":"2023","unstructured":"Gasser M, Naguib A, Abdelhafiz MM et al (2023) Artificial neural network model to predict filtrate invasion of nanoparticle-based drilling fluids. Trends Sci 20:12\u201314. https:\/\/doi.org\/10.48048\/tis.2023.6736","journal-title":"Trends Sci"},{"key":"2113_CR25","doi-asserted-by":"publisher","first-page":"12844","DOI":"10.1021\/ACSSUSCHEMENG.1C03563\/SUPPL_FILE\/SC1C03563_SI_001.PDF","volume":"9","author":"H Movahedi","year":"2021","unstructured":"Movahedi H, Jamshidi S, Hajipour M (2021) New insight into the filtration control of drilling fluids using a graphene-based nanocomposite under static and dynamic conditions. ACS Sustain Chem Eng 9:12844\u201312857. https:\/\/doi.org\/10.1021\/ACSSUSCHEMENG.1C03563\/SUPPL_FILE\/SC1C03563_SI_001.PDF","journal-title":"ACS Sustain Chem Eng"},{"key":"2113_CR26","doi-asserted-by":"publisher","first-page":"2506","DOI":"10.1021\/ACS.ENERGYFUELS.0C03258\/ASSET\/IMAGES\/ACS.ENERGYFUELS.0C03258.SOCIAL.JPEG_V03","volume":"35","author":"X Li","year":"2021","unstructured":"Li X, Jiang G, He Y, Chen G (2021) Novel starch composite fluid loss additives and their applications in environmentally friendly water-based drilling fluids. Energy Fuels 35:2506\u20132513. https:\/\/doi.org\/10.1021\/ACS.ENERGYFUELS.0C03258\/ASSET\/IMAGES\/ACS.ENERGYFUELS.0C03258.SOCIAL.JPEG_V03","journal-title":"Energy Fuels"},{"key":"2113_CR27","doi-asserted-by":"publisher","first-page":"934","DOI":"10.5829\/IJE.2020.33.05B.26","volume":"33","author":"E Leusheva","year":"2020","unstructured":"Leusheva E, Morenov V, Moradi ST (2020) Effect of carbonate additives on dynamic filtration index of drilling mud. Int J Eng 33:934\u2013939. https:\/\/doi.org\/10.5829\/IJE.2020.33.05B.26","journal-title":"Int J Eng"},{"key":"2113_CR28","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1080\/19942060.2022.2026821","volume":"16","author":"L Esfahanizadeh","year":"2022","unstructured":"Esfahanizadeh L, Dabir B, Goharpey F (2022) CFD modeling of the flow behavior around a PDC drill bit: effects of nano-enhanced drilling fluids on cutting transport and cooling efficiency. Eng Appl Comput Fluid Mech 16:977\u2013994. https:\/\/doi.org\/10.1080\/19942060.2022.2026821","journal-title":"Eng Appl Comput Fluid Mech"},{"key":"2113_CR29","doi-asserted-by":"crossref","unstructured":"Bridges S, Robinson LH (2020) A practical handbook for drilling fluids processing. Gulf Professional","DOI":"10.1016\/B978-0-12-821341-4.00011-7"},{"key":"2113_CR30","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/J.CEJ.2016.11.098","volume":"311","author":"SMR Shaikh","year":"2017","unstructured":"Shaikh SMR, Nasser MS, Hussein IA, Benamor A (2017) Investigation of the effect of polyelectrolyte structure and type on the electrokinetics and flocculation behavior of bentonite dispersions. Chem Eng J 311:265\u2013276. https:\/\/doi.org\/10.1016\/J.CEJ.2016.11.098","journal-title":"Chem Eng J"},{"key":"2113_CR31","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/J.SEPPUR.2017.06.050","volume":"187","author":"SMR Shaikh","year":"2017","unstructured":"Shaikh SMR, Nasser MS, Hussein I et al (2017) Influence of polyelectrolytes and other polymer complexes on the flocculation and rheological behaviors of clay minerals: a comprehensive review. Sep Purif Technol 187:137\u2013161. https:\/\/doi.org\/10.1016\/J.SEPPUR.2017.06.050","journal-title":"Sep Purif Technol"},{"key":"2113_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/J.CEJ.2022.136680","volume":"445","author":"J Liu","year":"2022","unstructured":"Liu J, Zhang T, Sun Y et al (2022) Insights into the high temperature-induced failure mechanism of bentonite in drilling fluid. Chem Eng J 445:136680. https:\/\/doi.org\/10.1016\/J.CEJ.2022.136680","journal-title":"Chem Eng J"},{"key":"2113_CR33","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3969\/J.ISSN.1001-5620.2015.04.006","volume":"32","author":"P Liangchun","year":"2015","unstructured":"Liangchun P, Mingyi D, Hongming L et al (2015) Effect of three new high temperature filtrate reducers on particle size distribution of drilling fluid. Drill Fluid Complet Fluid 32:21\u201324. https:\/\/doi.org\/10.3969\/J.ISSN.1001-5620.2015.04.006","journal-title":"Drill Fluid Complet Fluid"},{"issue":"7","key":"2113_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-16415-0","volume":"71","author":"A Gonz\u00e1lez Garc\u00eda","year":"2017","unstructured":"Gonz\u00e1lez Garc\u00eda A, Wensink HH, Lekkerkerker HNW (2017) Tuinier R (2017) Entropic patchiness drives multi-phase coexistence in discotic colloid\u2013depletant mixtures. Sci Reports 71(7):1\u20139. https:\/\/doi.org\/10.1038\/s41598-017-16415-0","journal-title":"Sci Reports"},{"key":"2113_CR35","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.petrol.2017.12.059","volume":"162","author":"S Davoodi","year":"2018","unstructured":"Davoodi S, Ramazani SAA, Jamshidi S, Fellah Jahromi A (2018) A novel field applicable mud formula with enhanced fluid loss properties in High Pressure-High Temperature well condition containing pistachio shell powder. J Pet Sci Eng 162:378\u2013385. https:\/\/doi.org\/10.1016\/j.petrol.2017.12.059","journal-title":"J Pet Sci Eng"},{"key":"2113_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/J.MOLLIQ.2022.121117","author":"S Davoodi","year":"2022","unstructured":"Davoodi S, Al-Shargabi M, Woodc DA et al (2022) Thermally stable and salt-resistant synthetic polymers as drilling fluid additives for deployment in harsh sub-surface conditions: a review. J Mol Liq. https:\/\/doi.org\/10.1016\/J.MOLLIQ.2022.121117","journal-title":"J Mol Liq"},{"key":"2113_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/J.PETSCI.2023.08.015","volume":"21","author":"S Davoodi","year":"2023","unstructured":"Davoodi S, Al-Shargabi M, Wood DA et al (2023) Synthetic polymers: a review of applications in drilling fluids. Pet Sci 21:1\u2013148. https:\/\/doi.org\/10.1016\/J.PETSCI.2023.08.015","journal-title":"Pet Sci"},{"key":"2113_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/J.MOLLIQ.2022.118725","volume":"352","author":"M Al-Shargabi","year":"2022","unstructured":"Al-Shargabi M, Davoodi S, Wood DA et al (2022) Nanoparticle applications as beneficial oil and gas drilling fluid additives: a review. J Mol Liq 352:118725. https:\/\/doi.org\/10.1016\/J.MOLLIQ.2022.118725","journal-title":"J Mol Liq"},{"key":"2113_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/J.FUEL.2023.129692","volume":"357","author":"S Davoodi","year":"2024","unstructured":"Davoodi S, Al-Shargabi M, Wood DA, Rukavishnikov VS (2024) Recent advances in polymers as additives for wellbore cementing applications: a review. Fuel 357:129692. https:\/\/doi.org\/10.1016\/J.FUEL.2023.129692","journal-title":"Fuel"},{"key":"2113_CR40","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/NECO.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y, Boser B, Denker JS et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541\u2013551. https:\/\/doi.org\/10.1162\/NECO.1989.1.4.541","journal-title":"Neural Comput"},{"key":"2113_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/J.PETROL.2021.108838","volume":"205","author":"L Shan","year":"2021","unstructured":"Shan L, Liu Y, Tang M et al (2021) CNN-BiLSTM hybrid neural networks with attention mechanism for well log prediction. J Pet Sci Eng 205:108838. https:\/\/doi.org\/10.1016\/J.PETROL.2021.108838","journal-title":"J Pet Sci Eng"},{"key":"2113_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/J.YMSSP.2020.107398","volume":"151","author":"S Kiranyaz","year":"2021","unstructured":"Kiranyaz S, Avci O, Abdeljaber O et al (2021) 1D convolutional neural networks and applications: A survey. Mech Syst Signal Process 151:107398. https:\/\/doi.org\/10.1016\/J.YMSSP.2020.107398","journal-title":"Mech Syst Signal Process"},{"key":"2113_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/J.ENERGY.2022.125270","volume":"261","author":"J Wang","year":"2022","unstructured":"Wang J, Cao J, Fu J, Xu H (2022) Missing well logs prediction using deep learning integrated neural network with the self-attention mechanism. Energy 261:125270. https:\/\/doi.org\/10.1016\/J.ENERGY.2022.125270","journal-title":"Energy"},{"key":"2113_CR44","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2022","unstructured":"Li Z, Liu F, Yang W et al (2022) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 33:6999\u20137019. https:\/\/doi.org\/10.1109\/TNNLS.2021.3084827","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2113_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/J.UNCRES.2021.04.001","volume":"1","author":"S Asante-Okyere","year":"2021","unstructured":"Asante-Okyere S, Ziggah YY, Marfo SA (2021) Improved total organic carbon convolutional neural network model based on mineralogy and geophysical well log data. Unconv Resour 1:1\u20138. https:\/\/doi.org\/10.1016\/J.UNCRES.2021.04.001","journal-title":"Unconv Resour"},{"key":"2113_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/J.SAA.2022.120961","volume":"272","author":"M Hamed Mozaffari","year":"2022","unstructured":"Hamed Mozaffari M, Tay LL (2022) Overfitting ONE-DIMENSIONAL CONVOLUTIONAL NEURAL NETWORKS FOR RAMAN SPECTRA IDEntification. Spectrochim Acta Part A Mol Biomol Spectrosc 272:120961. https:\/\/doi.org\/10.1016\/J.SAA.2022.120961","journal-title":"Spectrochim Acta Part A Mol Biomol Spectrosc"},{"key":"2113_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/neco.1997.9.1.1","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Flat minima. Neural Comput 9:1\u201342. https:\/\/doi.org\/10.1162\/neco.1997.9.1.1","journal-title":"Neural Comput"},{"key":"2113_CR48","doi-asserted-by":"publisher","DOI":"10.1109\/ICMEW.2018.8551584","author":"K Xu","year":"2018","unstructured":"Xu K, Shen X, Yao T et al (2018) Greedy layer-wise training of long short term memory networks. IEEE Int Conf Multimed Expo Work ICMEW. https:\/\/doi.org\/10.1109\/ICMEW.2018.8551584","journal-title":"IEEE Int Conf Multimed Expo Work ICMEW"},{"key":"2113_CR49","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.2c06308","author":"H Ji","year":"2022","unstructured":"Ji H, Lou Y, Cheng S et al (2022) An advanced long short-term memory (LSTM) neural network method for predicting rate of penetration (ROP). ACS Omega. https:\/\/doi.org\/10.1021\/acsomega.2c06308","journal-title":"ACS Omega"},{"key":"2113_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/J.PETROL.2021.110047","volume":"210","author":"X Li","year":"2022","unstructured":"Li X, Xiao K, Li X et al (2022) A well rate prediction method based on LSTM algorithm considering manual operations. J Pet Sci Eng 210:110047. https:\/\/doi.org\/10.1016\/J.PETROL.2021.110047","journal-title":"J Pet Sci Eng"},{"key":"2113_CR51","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/J.ENBUILD.2016.04.021","volume":"122","author":"S Sajjadi","year":"2016","unstructured":"Sajjadi S, Shamshirband S, Alizamir M et al (2016) Extreme learning machine for prediction of heat load in district heating systems. Energy Build 122:222\u2013227. https:\/\/doi.org\/10.1016\/J.ENBUILD.2016.04.021","journal-title":"Energy Build"},{"key":"2113_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/J.PETROL.2021.109686","volume":"208","author":"R Huang","year":"2022","unstructured":"Huang R, Wei C, Wang B et al (2022) Well performance prediction based on Long Short-Term Memory (LSTM) neural network. J Pet Sci Eng 208:109686. https:\/\/doi.org\/10.1016\/J.PETROL.2021.109686","journal-title":"J Pet Sci Eng"},{"key":"2113_CR53","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/J.NEUCOM.2015.06.083","volume":"172","author":"E Emary","year":"2016","unstructured":"Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371\u2013381. https:\/\/doi.org\/10.1016\/J.NEUCOM.2015.06.083","journal-title":"Neurocomputing"},{"key":"2113_CR54","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/J.ADVENGSOFT.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46\u201361. https:\/\/doi.org\/10.1016\/J.ADVENGSOFT.2013.12.007","journal-title":"Adv Eng Softw"},{"key":"2113_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/J.JNGSE.2020.103695","volume":"85","author":"K Biswas","year":"2021","unstructured":"Biswas K, Vasant PM, Gamez Vintaned JA, Watada J (2021) Cellular automata-based multi-objective hybrid grey wolf optimization and particle swarm optimization algorithm for wellbore trajectory optimization. J Nat Gas Sci Eng 85:103695. https:\/\/doi.org\/10.1016\/J.JNGSE.2020.103695","journal-title":"J Nat Gas Sci Eng"},{"key":"2113_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/J.PETSCI.2023.09.001","author":"S Deng","year":"2023","unstructured":"Deng S, Pan H-Y, Wang H-G et al (2023) A hybrid machine learning optimization algorithm for multivariable pore pressure prediction. Pet Sci. https:\/\/doi.org\/10.1016\/J.PETSCI.2023.09.001","journal-title":"Pet Sci"},{"key":"2113_CR57","doi-asserted-by":"publisher","first-page":"8995","DOI":"10.1029\/JC090IC05P08995","volume":"90","author":"CJ Willmott","year":"1985","unstructured":"Willmott CJ, Ackleson SG, Davis RE et al (1985) Statistics for the evaluation and comparison of models. J Geophys Res Ocean 90:8995\u20139005. https:\/\/doi.org\/10.1029\/JC090IC05P08995","journal-title":"J Geophys Res Ocean"},{"key":"2113_CR58","doi-asserted-by":"publisher","first-page":"7643","DOI":"10.1016\/J.EGYR.2022.06.003","volume":"8","author":"H Vo Thanh","year":"2022","unstructured":"Vo Thanh H, Safaei-Farouji M, Wei N et al (2022) Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals. Energy Rep 8:7643\u20137656. https:\/\/doi.org\/10.1016\/J.EGYR.2022.06.003","journal-title":"Energy Rep"},{"key":"2113_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/J.IJRMMS.2023.105546","volume":"170","author":"S Davoodi","year":"2023","unstructured":"Davoodi S, Mehrad M, Wood DA et al (2023) Predicting uniaxial compressive strength from drilling variables aided by hybrid machine learning. Int J Rock Mech Min Sci 170:105546. https:\/\/doi.org\/10.1016\/J.IJRMMS.2023.105546","journal-title":"Int J Rock Mech Min Sci"},{"key":"2113_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-023-46064-5","volume":"13","author":"S Hosseini","year":"2023","unstructured":"Hosseini S, Khatti J, Taiwo BO et al (2023) Assessment of the ground vibration during blasting in mining projects using different computational approaches. Sci Rep 13:1\u201329. https:\/\/doi.org\/10.1038\/s41598-023-46064-5","journal-title":"Sci Rep"},{"key":"2113_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/J.YMSSP.2020.107163","volume":"148","author":"L Colombo","year":"2021","unstructured":"Colombo L, Oboe D, Sbarufatti C et al (2021) Shape sensing and damage identification with iFEM on a composite structure subjected to impact damage and non-trivial boundary conditions. Mech Syst Signal Process 148:107163. https:\/\/doi.org\/10.1016\/J.YMSSP.2020.107163","journal-title":"Mech Syst Signal Process"},{"key":"2113_CR62","doi-asserted-by":"publisher","DOI":"10.1109\/CIS.2019.00025","author":"H Li","year":"2019","unstructured":"Li H, Li J, Guan X et al (2019) Research on overfitting of deep learning. Proc-2019 Int Conf Comput Intell Secur CIS. https:\/\/doi.org\/10.1109\/CIS.2019.00025","journal-title":"Proc-2019 Int Conf Comput Intell Secur CIS"},{"key":"2113_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/J.CEMCONRES.2021.106449","volume":"145","author":"PG Asteris","year":"2021","unstructured":"Asteris PG, Skentou AD, Bardhan A et al (2021) Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cem Concr Res 145:106449. https:\/\/doi.org\/10.1016\/J.CEMCONRES.2021.106449","journal-title":"Cem Concr Res"},{"key":"2113_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119796","volume":"222","author":"S Davoodi","year":"2023","unstructured":"Davoodi S, Vo Thanh H, Wood DA et al (2023) Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites. Expert Syst Appl 222:119796. https:\/\/doi.org\/10.1016\/j.eswa.2023.119796","journal-title":"Expert Syst Appl"},{"key":"2113_CR65","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1001\/jamapsychiatry.2019.3671","volume":"77","author":"RA Poldrack","year":"2020","unstructured":"Poldrack RA, Huckins G, Varoquaux G (2020) Establishment of best practices for evidence for prediction: a review. JAMA Psychiat 77:534\u2013540. https:\/\/doi.org\/10.1001\/jamapsychiatry.2019.3671","journal-title":"JAMA Psychiat"},{"key":"2113_CR66","unstructured":"Ribeiro MI (2004) Gaussian probability density functions: Properties and error characterization. Inst Super Tcnico, Lisboa, Port Tech Rep 1049\u2013001"},{"key":"2113_CR67","doi-asserted-by":"publisher","DOI":"10.1371\/JOURNAL.PONE.0272269","volume":"17","author":"MA Salam","year":"2022","unstructured":"Salam MA, El-Fatah MA, Hassan NF (2022) Automatic grading for Arabic short answer questions using optimized deep learning model. PLoS ONE 17:e0272269. https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0272269","journal-title":"PLoS ONE"},{"key":"2113_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/J.ESWA.2022.119497","volume":"216","author":"A Kumar","year":"2023","unstructured":"Kumar A, Arora HC, Kumar K, Garg H (2023) Performance prognosis of FRCM-to-concrete bond strength using ANFIS-based fuzzy algorithm. Expert Syst Appl 216:119497. https:\/\/doi.org\/10.1016\/J.ESWA.2022.119497","journal-title":"Expert Syst Appl"},{"key":"2113_CR69","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2023.3263739","author":"M Angelini","year":"2023","unstructured":"Angelini M, Blasilli G, Lenti S, Santucci G (2023) A visual analytics conceptual framework for explorable and steerable partial dependence analysis. IEEE Trans Vis Comput Graph. https:\/\/doi.org\/10.1109\/TVCG.2023.3263739","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"2113_CR70","doi-asserted-by":"publisher","DOI":"10.1016\/J.COMPCHEMENG.2023.108306","volume":"176","author":"T Danesh","year":"2023","unstructured":"Danesh T, Ouaret R, Floquet P, Negny S (2023) Hybridization of model-specific and model-agnostic methods for interpretability of Neural network predictions: application to a power plant. Comput Chem Eng 176:108306. https:\/\/doi.org\/10.1016\/J.COMPCHEMENG.2023.108306","journal-title":"Comput Chem Eng"},{"key":"2113_CR71","doi-asserted-by":"publisher","first-page":"4934","DOI":"10.3390\/en16134934","volume":"16","author":"A-S Al-Rubaii","year":"2023","unstructured":"Al-Rubaii A-S, Al-Shehri D (2023) A novel model for the real-time evaluation of hole-cleaning conditions with case studies. Energies 16:4934. https:\/\/doi.org\/10.3390\/en16134934","journal-title":"Energies"}],"container-title":["Engineering with Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-025-02113-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00366-025-02113-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00366-025-02113-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T23:29:16Z","timestamp":1758065356000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00366-025-02113-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,23]]},"references-count":71,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2113"],"URL":"https:\/\/doi.org\/10.1007\/s00366-025-02113-3","relation":{},"ISSN":["0177-0667","1435-5663"],"issn-type":[{"type":"print","value":"0177-0667"},{"type":"electronic","value":"1435-5663"}],"subject":[],"published":{"date-parts":[[2025,2,23]]},"assertion":[{"value":"3 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}