{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:48:37Z","timestamp":1779896917584,"version":"3.53.1"},"reference-count":32,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T00:00:00Z","timestamp":1565308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Apparent viscosity is of one of the main rheological properties of drilling fluid. Monitoring apparent viscosity during drilling operations is very important to prevent various drilling problems and improve well cleaning efficiency. Apparent viscosity can be measured in the laboratory using rheometer or viscometer devices. However, this laboratory measurement is a time-consuming operation. Thus, in this paper, we have developed a new empirical correlation and a new artificial neural network model to predict the apparent viscosity of drilling fluid as a function of two simple and fast measurements of drilling mud (i.e., March funnel viscosity and mud density). 142 experimental measurements for different drilling mud samples have been used to develop the new correlation. The calculated apparent viscosity from the developed correlation and neural network model has been compared with the measured apparent viscosity from the laboratory. The results show that the developed correlation and neural network model predict the apparent viscosity with very good accuracy. The new correlation and neural network models predict the apparent viscosity with a correlation coefficient (R) of 98.8% and 98.1% and an average absolute error (AAE) of 8.6% and 10.9%, respectively, compared to the R of 89.2% and AAE of 20.3% if the literature correlations are used. Thus, we conclude that the newly developed correlation and artificial neural network (ANN) models are preferable to predict the apparent viscosity of drilling fluid.<\/jats:p>","DOI":"10.3390\/en12163067","type":"journal-article","created":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T11:11:31Z","timestamp":1565349091000},"page":"3067","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-9869","authenticated-orcid":false,"given":"Emad A.","family":"Al-Khdheeawi","sequence":"first","affiliation":[{"name":"Department of Petroleum Engineering, Curtin University, Kensington 6151, Australia"},{"name":"Petroleum Technology Department, University of Technology, Baghdad 10066, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Doaa Saleh","family":"Mahdi","sequence":"additional","affiliation":[{"name":"Petroleum Technology Department, University of Technology, Baghdad 10066, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,9]]},"reference":[{"key":"ref_1","unstructured":"Caenn, R., Darley, H.C., and Gray, G.R. (2011). Composition and Properties of Drilling and Completion Fluids, Gulf Professional Publishing."},{"key":"ref_2","unstructured":"Darley, H.C., and Gray, G.R. (1988). Composition and Properties of Drilling and Completion Fluids, Gulf Professional Publishing."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.petrol.2011.04.008","article-title":"Rheological and yield stress measurements of non-Newtonian fluids using a Marsh Funnel","volume":"77","author":"Balhoff","year":"2011","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_4","unstructured":"ASME Shale Shaker Committee (2011). Drilling Fluids Processing Handbook, Elsevier."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bourgoyne, A.T., Millheim, K.K., Chenevert, M.E., and Young, F.S. (1986). Applied Drilling Engineering, Society of Petroleum Engineers.","DOI":"10.2118\/9781555630010"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"35","DOI":"10.2118\/950035-G","article-title":"Ability of drilling mud to lift bit cuttings","volume":"2","author":"Hall","year":"1950","journal-title":"J. Pet. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.2118\/10674-PA","article-title":"Experimental study of drilled cuttings transport using common drilling muds","volume":"23","author":"Hussaini","year":"1983","journal-title":"Soc. Pet. Eng. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.petrol.2019.01.063","article-title":"Experimental investigation of hole cleaning in directional drilling by using nano-enhanced water-based drilling fluids","volume":"176","author":"Boyou","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.petrol.2017.11.067","article-title":"Effect of nanoparticles on the modifications of drilling fluids properties: A review of recent advances","volume":"161","author":"Rafati","year":"2018","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.colsurfa.2017.10.050","article-title":"Application of aluminium oxide nanoparticles to enhance rheological and filtration properties of water based muds at HPHT conditions","volume":"537","author":"Smith","year":"2018","journal-title":"Colloids Surf. A Physicochem. Eng. Asp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2118\/62020-PA","article-title":"The Marsh funnel and drilling fluid viscosity: A new equation for field use","volume":"15","author":"Pitt","year":"2000","journal-title":"SPE Drill. Complet."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.petrol.2016.08.021","article-title":"Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)","volume":"146","author":"Elkatatny","year":"2016","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.petrol.2011.06.024","article-title":"Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields","volume":"78","author":"Asadisaghandi","year":"2011","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_14","first-page":"408","article-title":"Application of adaptive neuro-fuzzy inference system for grade estimation; case study, sarcheshmeh porphyry copper deposit, Kerman, Iran","volume":"4","author":"Tahmasebi","year":"2010","journal-title":"Aust. J. Basic Appl. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s11053-011-9135-3","article-title":"Application of a modular feedforward neural network for grade estimation","volume":"20","author":"Tahmasebi","year":"2011","journal-title":"Nat. Resour. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.cageo.2012.02.004","article-title":"A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation","volume":"42","author":"Tahmasebi","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.petrol.2012.03.019","article-title":"A fast and independent architecture of artificial neural network for permeability prediction","volume":"86","author":"Tahmasebi","year":"2012","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Le Van, S., and Chon, B. (2016). Artificial neural network model for alkali-surfactant-polymer flooding in viscous oil reservoirs: Generation and application. Energies, 9.","DOI":"10.3390\/en9121081"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Le Van, S., and Chon, B. (2017). Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO2 Performance. Energies, 10.","DOI":"10.3390\/en10070842"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.petrol.2017.07.034","article-title":"Evaluating the critical performances of a CO2-Enhanced oil recovery process using artificial neural network models","volume":"157","author":"Chon","year":"2017","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"032906","DOI":"10.1115\/1.4038054","article-title":"Effective prediction and management of a CO2 flooding process for enhancing oil recovery using artificial neural networks","volume":"140","author":"Chon","year":"2018","journal-title":"J. Energy Resour. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.2118\/58046-JPT","article-title":"Virtual-intelligence applications in petroleum engineering: Part 1\u2014Artificial neural networks","volume":"52","author":"Mohaghegh","year":"2000","journal-title":"J. Pet. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1007\/s11814-015-0025-y","article-title":"Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature","volume":"32","author":"Mohagheghian","year":"2015","journal-title":"Korean J. Chem. Eng."},{"key":"ref_24","unstructured":"Al-Khdheeawi, E.A., Feng, R., and Mahdi, D.S. (2019, January 23\u201326). Lithology Determination from Drilling Data Using Artificial Neural Network. Proceedings of the 53rd US Rock Mechanics\/Geomechanics Symposium, New York, NY, USA."},{"key":"ref_25","first-page":"54","article-title":"Undersaturated Oil Compressibility Prediction for Mishrif Reservoir in the Southern Iraqi Oil Fields Using Artificial Neural Network","volume":"377","author":"Hussain","year":"2014","journal-title":"J. Pet. Res. Stud."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ramgulam, A., Ertekin, T., and Flemings, P.B. (2006). Utilization of Artificial Neural Networks in the Optimization of History Matching. [Ph.D. Thesis, Pennsylvania State University].","DOI":"10.2118\/107468-MS"},{"key":"ref_27","unstructured":"American Petroleum Institute (2017). Recommended Practice for Field Testing Water-based Drilling Fluids, American Petroleum Institute."},{"key":"ref_28","unstructured":"Skelland, A.H.P. (1967). Non-Newtonian Flow and Heat Transfer, Wiley."},{"key":"ref_29","unstructured":"Bingham, E.C. (1922). Fluidity and Plasticity, McGraw-Hill."},{"key":"ref_30","unstructured":"Azar, J.J., and Samuel, G.R. (2007). Drilling Engineering, PennWell books."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2118\/931234-G","article-title":"Properties and treatment of rotary mud","volume":"92","author":"Marsh","year":"1931","journal-title":"Trans. AIME"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.31026\/j.eng.2013.09.09","article-title":"New Correlation for Predicting Undersaturated Oil Compressibility for Mishrif Reservoir in the Southern Iraqi Oil Fields","volume":"19","author":"Baker","year":"2013","journal-title":"J. Eng."}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/12\/16\/3067\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:09:47Z","timestamp":1760188187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/12\/16\/3067"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,9]]},"references-count":32,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["en12163067"],"URL":"https:\/\/doi.org\/10.3390\/en12163067","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,9]]}}}