Apparent Viscosity Prediction of Water-Based Muds Using Empirical Correlation and an Artificial Neural Network

被引:26
作者
Al-Khdheeawi, Emad A. [1 ,2 ]
Mahdi, Doaa Saleh [2 ]
机构
[1] Curtin Univ, Dept Petr Engn, Kensington, NSW 6151, Australia
[2] Univ Technol Baghdad, Petr Technol Dept, Baghdad 10066, Iraq
关键词
rheological properties; drilling mud; apparent viscosity; marsh funnel; mud weight; NANOPARTICLES;
D O I
10.3390/en12163067
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:10
相关论文
共 32 条
  • [1] [Anonymous], 2011, NAT RESOUR RES
  • [2] [Anonymous], 2007, Drilling Engineering
  • [3] [Anonymous], 2006, THESIS
  • [4] [Anonymous], 1922, FLUIDITY PLASTICITY
  • [5] Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields
    Asadisaghandi, Jalil
    Tahmasebi, Pejman
    [J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2011, 78 (02) : 464 - 475
  • [6] *ASME SHAL SHAK CO, 2011, DRILL FLUIDS PROC HD
  • [7] Rheological and yield stress measurements of non-Newtonian fluids using a Marsh Funnel
    Balhoff, Matthew T.
    Lake, Larry W.
    Bommer, Paul M.
    Lewis, Rebecca E.
    Weber, Mark J.
    Calderin, Jennifer M.
    [J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2011, 77 (3-4) : 393 - 402
  • [8] Bourgoyne A.T., 1986, APPL DRILLING ENG
  • [9] Experimental investigation of hole cleaning in directional drilling by using nano-enhanced water-based drilling fluids
    Boyou, Natalie Vanessa
    Ismail, Issham
    Sulaiman, Wan Rosli Wan
    Haddad, Amin Sharifi
    Husein, Norhafizuddin
    Hui, Heah Thin
    Nadaraja, Kathigesu
    [J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2019, 176 : 220 - 231
  • [10] Caenn R, 2011, COMPOSITION AND PROPERTIES OF DRILLING AND COMPLETION FLUIDS, 6TH EDITION, P1