Research Papers: Petroleum Engineering

Real-Time Determination of Rheological Properties of Spud Drilling Fluids Using a Hybrid Artificial Intelligence Technique

[+] Author and Article Information
Khaled Abdelgawad

Department of Petroleum Engineering,
King Fahd University of Petroleum & Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: abouzidan@kfupm.edu.sa

Salaheldin Elkatatny

Department of Petroleum Engineering,
King Fahd University of Petroleum & Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: elkatatny@kfupm.edu.sa

Tamer Moussa

Department of Petroleum Engineering,
King Fahd University of Petroleum & Minerals,
Dhahran 31261, Saudi Arabia
e-mail: g201105270@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering,
King Fahd University of Petroleum & Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: mmahmoud@kfupm.edu.sa

Shirish Patil

Department of Petroleum Engineering,
King Fahd University of Petroleum & Minerals,
P.O. Box 5049,
Dhahran 31261, Saudi Arabia
e-mail: patil@kfupm.edu.sa

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received May 26, 2018; final manuscript received December 3, 2018; published online January 9, 2019. Assoc. Editor: Fanhua Zeng.

J. Energy Resour. Technol 141(3), 032908 (Jan 09, 2019) (9 pages) Paper No: JERT-18-1379; doi: 10.1115/1.4042233 History: Received May 26, 2018; Revised December 03, 2018

The rheological properties of the drilling fluid play a key role in controlling the drilling operation. Knowledge of drilling fluid rheological properties is very crucial for drilling hydraulic calculations required for hole cleaning optimization. Measuring the rheological properties during drilling sometimes is a time-consuming process. Wrong estimation of these properties may lead to many problems, such as pipe sticking, loss of circulation, and/or well control issues. The aforementioned problems increase the non-productive time and the overall cost of the drilling operations. In this paper, the frequent drilling fluid measurements (mud density, Marsh funnel viscosity (MFV), and solid percent) are used to estimate the rheological properties of bentonite spud mud. Artificial neural network (ANN) technique was combined with the self-adaptive differential evolution algorithm (SaDe) to develop an optimum ANN model for each rheological property using 1029 data points. The SaDe helped to optimize the best combination of parameters for the ANN models. For the first time, based on the developed ANN models, empirical equations are extracted for each rheological parameter. The ANN models predicted the rheological properties from the mud density, MFV, and solid percent with high accuracy (average absolute percentage error (AAPE) less than 5% and correlation coefficient higher than 95%). The developed apparent viscosity model was compared with the available models in the literature using the unseen dataset. The SaDe-ANN model outperformed the other models which overestimated the apparent viscosity of the spud drilling fluid. The developed models will help drilling engineers to predict the rheological properties every 15–20 min. This will help to optimize hole cleaning and avoid pipe sticking and loss of circulation where bentonite spud mud is used. No additional equipment or special software is required for applying the new method.

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Grahic Jump Location
Fig. 1

Marsh funnel viscometer [13]

Grahic Jump Location
Fig. 2

Correlation coefficient between input parameters (measured drilling fluid properties) and output parameters (rheological properties); MW—mud weight, MFV—Marsh funnel viscosity, and SV—solid volume

Grahic Jump Location
Fig. 3

Prediction of rheological properties using SaDe-ANN models for training data (633 data points)

Grahic Jump Location
Fig. 4

Prediction of rheological properties using SaDe-ANN models for testing data (246 data points)

Grahic Jump Location
Fig. 5

Prediction of rheological properties using SaDe-ANN models for validation data (150 data points)

Grahic Jump Location
Fig. 6

Comparison of apparent viscosity SaDe-ANN model with published apparent viscosity models



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