{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:53:10Z","timestamp":1786978390549,"version":"build-2736575974"},"reference-count":58,"publisher":"American Chemical Society (ACS)","issue":"6","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>One of the functions of drilling mud is to transport cuttings out of a drilled well. To fulfill this purpose, the mud must possess optimal viscous characteristics. The main goal of this study is to develop a model for estimating the plastic viscosity (PV) of oil-based muds (OBMs) at downhole conditions, especially when high-temperature, high-pressure wells are drilled. Existing methods used by previous researchers include the following: the multiplicative factor method, the relative dial readings approach, and the nonlinear regression technique. These methods do not result in flexible, generalizable, accurate, or robust models. Furthermore, existing literature mostly contains models that are only applicable under normal temperature and pressure conditions (surface conditions). To overcome these challenges, this study employed the artificial neural network (ANN) technique to predict the PV of OBM under downhole conditions. The data used to develop the model consists of 88 OBM laboratory PV measurements obtained from literature. The findings indicate that the performance of the developed model using the statistical metrics of means square error (MSE), root-mean-square error (RMSE), and correlation coefficient (R) was 0.0185, 0.136, and 0.967 respectively. To test for the generalizability of the developed model, a new data set consisting of 56 data points was used. In this regard, the model had an R value of 0.80, an MSE of 0.95, and an RMSE of 0.975. In order to ascertain the parametric importance of the inputs, a connection weight algorithm was utilized. In this regard, the downhole pressure had a higher influence on the PV (64.5%) than that of downhole temperature (35.5%). To make the models simple to incorporate in software applications, they were explicitly presented. In terms of memory requirements and processing speed for software applications, the models had a memory footprint of 48 bytes and required 12 floating-point operations to give an output. This model is valid for 20 \u00b0C \u2264 temperature \u2264315 \u00b0C and 0 psi \u2264 pressure \u226440,000 psi. The characteristics of the models proposed in this work for which originality is claimed include the models\u2019 computational cost evaluation, their explicitness, and a suggestion for using them in the field. Upon utilizing the proposed models, time-consuming laboratory measurements of PV would no longer be necessary, and real-time results could be provided in the field.<\/jats:p>","DOI":"10.1021\/acsomega.3c07815","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T03:07:55Z","timestamp":1706584075000},"page":"6684-6695","source":"Crossref","is-referenced-by-count":19,"title":["Explicit Data-Based Model for Predicting Oil-Based\nMud Viscosity at Downhole Conditions"],"prefix":"10.1021","volume":"9","author":[{"given":"Ahmad","family":"Alkouh","sequence":"first","affiliation":[{"name":"PAAET , , ,","place":["Kuwait City, Kuwait, 70654"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9072-1543","authenticated-orcid":true,"given":"Khaled","family":"Elraies","sequence":"additional","affiliation":[{"name":"Universiti Teknologi PETRONAS , , , ,","place":["Seri Iskandar, Perak Darul Ridzuan, Malaysia, 32610"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okorie Ekwe","family":"Agwu","sequence":"additional","affiliation":[{"name":"Universiti Teknologi PETRONAS , , , ,","place":["Seri Iskandar, Perak Darul Ridzuan, Malaysia, 32610"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0883-4408","authenticated-orcid":true,"given":"Saad","family":"Alatefi","sequence":"additional","affiliation":[{"name":"PAAET , , ,","place":["Kuwait City, Kuwait, 70654"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reda Abdel","family":"Azim","sequence":"additional","affiliation":[{"name":"American University of Kurdistan , , ,","place":["Sumel, Iraq, 42003"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"2026081709185140200_cit1","unstructured":"Smithson, T.\n          \n          The Defining Series:\nHPHT Wells, Oilfield Review, 2016, https:\/\/www.slb.com\/resource-library\/oilfield-review\/defining-series\/defining-hpht (retrieved April 10, 2022)."},{"key":"2026081709185140200_cit2","doi-asserted-by":"publisher","first-page":"102066","DOI":"10.1016\/j.geothermics.2021.102066","article-title":"Significance and complications of\ndrilling fluid rheology in geothermal drilling: A review","volume":"93","author":"Mohamed","year":"2021","journal-title":"Geothermics"},{"key":"2026081709185140200_cit3","first-page":"149","volume-title":"Geothermal Drilling\nFluids","author":"Chemwotei","year":"2011"},{"key":"2026081709185140200_cit4","doi-asserted-by":"publisher","first-page":"25","DOI":"10.4236\/ojogas.2021.62003","article-title":"Development of a High Temperature\nand High-Pressure Oil-Based Drilling\nFluid Emulsion Stability Tester","volume":"06","author":"Long","year":"2021","journal-title":"Open J. Yangtze\nOil Gas"},{"issue":"3\u20134","key":"2026081709185140200_cit5","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.petrol.2011.04.008","article-title":"Rheological and\nyield stress measurements of non-Newtonian\nfluids using a Marsh funnel","volume":"77","author":"Balhoff","year":"2011","journal-title":"J. Pet. Sci. Eng."},{"key":"2026081709185140200_cit6","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.petrol.2013.03.027","article-title":"Rheological\nanalysis of drilling\nfluid using Marsh Funnel","volume":"105","author":"Guria","year":"2013","journal-title":"J. Pet. Sci. Eng."},{"issue":"1","key":"2026081709185140200_cit7","doi-asserted-by":"publisher","first-page":"58","DOI":"10.37624\/IJERT\/13.1.2020.58-65","article-title":"Measurement of the Plastic Viscosity\nand Yield Point\nof Drilling Fluids","volume":"13","author":"Delikesheva","year":"2020","journal-title":"Int. J. Eng. Res. Technol."},{"key":"2026081709185140200_cit8","doi-asserted-by":"crossref","unstructured":"Elkatatny, S.; Mousa, T.; Mahmoud, M.\n          A\nnew approach to determine\nthe rheology parameters for water-based drilling fluid using artificial\nneural network. Paper SPE-192190-MS Presented\nat the SPE Kingdom of Saudi Arabia Annual Technical Symposium and\nExhibition: Dammam, Saudi Arabia, 2018.","DOI":"10.2118\/192190-MS"},{"key":"2026081709185140200_cit9","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.3390\/en12101880","article-title":"Data-Driven\nFramework to Predict the Rheological Properties of CaCl2 Brine-Based\nDrill-in Fluid Using Artificial Neural Network","volume":"12","author":"Gowida","year":"2019","journal-title":"Energies"},{"key":"2026081709185140200_cit10","doi-asserted-by":"publisher","first-page":"103224","DOI":"10.1016\/j.jngse.2020.103224","article-title":"Real-time determination of rheological\nproperties of high over-balanced drilling fluid used for drilling\nultra-deep gas wells using artificial neural network","volume":"77","author":"Gomaa","year":"2020","journal-title":"J. Nat. Gas Sci. 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Paper Presented at the International Conference on Learning\nRepresentations ICLR 2021: Vienna, Austria, 2021."},{"key":"2026081709185140200_cit53","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.neucom.2020.03.114","article-title":"A novel method\nto compute the weights\nof neural networks","volume":"407","author":"Gao","year":"2020","journal-title":"Neurocomputing"},{"key":"2026081709185140200_cit54","unstructured":"Justus, D.; Brennan, J.; Bonner, S.; McGough, A. 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Paper SPE-192199-MS Presented\nat the SPE Kingdom of Saudi Arabia Annual Technical Symposium and\nExhibition: Dammam, Saudi Arabia, 2018.","DOI":"10.2118\/192199-MS"},{"issue":"18","key":"2026081709185140200_cit57","doi-asserted-by":"publisher","first-page":"5008","DOI":"10.3390\/su11185008","article-title":"Real-Time\nPrediction of the Rheological Properties of Water-Based Drill-In Fluid\nUsing Artificial Neural Networks","volume":"11","author":"Elkatatny","year":"2019","journal-title":"Sustainability"},{"issue":"48","key":"2026081709185140200_cit58","doi-asserted-by":"publisher","first-page":"32948","DOI":"10.1021\/acsomega.1c04937","article-title":"Prediction of the\nRheological Properties of Invert Emulsion Mud Using an Artificial\nNeural Network","volume":"6","author":"Gouda","year":"2021","journal-title":"ACS Omega"}],"container-title":["ACS Omega"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acsomega.3c07815","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/9\/6\/6684\/4435734\/ao3c07815.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/acsodf\/article-pdf\/9\/6\/6684\/4435734\/ao3c07815.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T13:53:53Z","timestamp":1786974833000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/acsodf\/article\/9\/6\/6684\/432630\/Explicit-Data-Based-Model-for-Predicting-Oil-Based"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":58,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,1,30]]},"published-print":{"date-parts":[[2024,2,13]]}},"URL":"https:\/\/doi.org\/10.1021\/acsomega.3c07815","relation":{},"ISSN":["2470-1343"],"issn-type":[{"value":"2470-1343","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,30]]}}}