0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

Caenn, R. , Darley, H. , and Gray, G. , 2017, Composition and Properties of Drilling and Completion Fluids, 7th ed., Gulf Professional Publishing, Waltham, MA.
Schlumberger Oilfield Glossary, 2018, “ Spud Mud,” accessed Mar. 13, 2018 https://www.glossary.oilfield.slb.com/en/Terms/s/spud_mud.aspx
Cheraghian, G. , Wu, Q. , Mostofi, M. , Li, M. , Afrand, M. , and Sangwai, J. S. , 2018, “ Effect of a Novel Clay/silica Nanocomposite on Water-Based Drilling Fluids: Improvements in Rheological and Filtration Properties,” Colloids Surf. A: Physicochem. Eng. Aspects, 555, pp. 339–350. [CrossRef]
Saasen, A. , Dahl, B. , and Jødestøl, K. , 2012, “ Particle Size Distribution of Top-Hole Drill Cuttings From Norwegian Sea Area Offshore Wells,” Part. Sci. Technol., 31(1), pp. 85–91. [CrossRef]
Cheraghian, G. , Hemmati, M. , and Bazgir, S. , 2014, “ Application of TiO2 and Fumed Silica Nanoparticles and Improve the Performance of Drilling Fluids,” AIP Conf. Proc., 1590(1), pp. 266–270.
Outmans, H. D. , 1957, “ Mechanics of Differential Pressure Sticking of Drill Collars,” Annual Fall Meeting of Southern California Petroleum Section in Los Angeles, CA, Oct. 17–18, SPE Paper No. SPE-963-G.
Bourgoyne, A. T. , Cheever, M. E. , Mulheim, K. K. , and Young, F. S. , 1991, Applied Drilling Engineering ( SPE Textbook Series, Vol. 2), Society of Petroleum Engineers, Richardson, TX.
Power, D. , and Zamora, M. , 2003, “ Drilling Fluid Yield Stress: Measurement Techniques for Improved Understanding of Critical Drilling Fluid Parameters,” AADE National Technology Conference: Practical Solutions for Drilling Challenges, Houston, TX, Apr. 1–3, Paper No. AADE-03-NTCE-35. http://www.aade.org/app/download/7238841177/AADE-03-NTCE-35-Power.pdf
Mitchell, R. F. , and Miska, S. Z. , 2011, Fundamentals of Drilling Engineering, Society of Petroleum Engineers, Richardson, TX.
Hussaini, S. M. , and Azar, J. J. , 1983, “ Experimental Study of Drilled Cutting Transport Using Common Drilling Muds,” SPE J., 23(1), pp. 11–20.
Cheraghian, G. , 2017, “ Application of Nano-Particles of Clay to Improve Drilling Fluid,” Int. J. Nanosci. Nanotechnol., 13(2), pp. 177–86. http://www.ijnnonline.net/article_25616.html
Mishra, D. , 2016, Drilling Fluids Processing Handbook, Scitus Academics, Valley Cottage, NY.
Marsh, H. , 1931, “ Properties and Treatment of Rotary Mud,” Trans. AIME., 92(01), pp. 234–251. [CrossRef]
Pitt, M. J. , 2000, “ The Marsh Funnel and Drilling Fluid Viscosity: A New Equation for Field Use,” SPE Drill. Completion., 15(1), pp. 3–6. [CrossRef]
Almahdawi, F. H. , Al-Yaseri, A. Z. , and Jasim, N. , 2014, “ Apparent Viscosity Direct From Marsh Funnel Test,” Iraqi J. Chem. Pet. Eng., 15(1), pp. 51–57. https://www.iasj.net/iasj?func=fulltext&aId=86359
Velazquez, G. J. , Escalona Quintero, C. J. , and Gimenez, E. R. , 2012, “ Production Monitoring Using Artificial Intelligence,” SPE Intelligent Energy International, Utrecht, The Netherlands, Mar. 27–29, SPE Paper No. SPE-149594-MS.
Weiss, W. W. , Balch, R. S. , and Stubbs, B. A. , 2002, “ How Artificial Intelligence Methods Can Forecast Oil Production,” SPE/DOE Improved Oil Recovery Symposium, Tulsa, OK, Apr. 13–17, SPE Paper No. SPE-75143-MS.
Al-arfaj, M. K. , Abdulraheem, A. , and Busaleh, Y. R. , 2012, “ Estimating Dewpoint Pressure Using Artificial Intelligence,” SPE Saudi Arabia Section Young Professionals Technical Symposium, Dhahran, Saudi Arabia, Mar. 19–21, SPE Paper No. SPE-160919-MS.
Elkatatny, S. M. , 2017, “ Real Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Network,” Arabian J. Sci. Eng., 42(4), pp. 1655–1665. [CrossRef]
Khaksar, A. , Rostami, H. , Moein, S. , and Rezaei, H. , 2016, “ Application of Artificial Neural Network–Particle Swarm Optimization Algorithm for Prediction of Gas Condensate Dew Point Pressure and Comparison With Gaussian Processes Regression–Particle Swarm Optimization Algorithm,” ASME J. Energy Resour. Technol., 138(3), p. 032903. [CrossRef]
AlAjmi, M. , Abdulraheem, A. , Mishkhes, A. T. , and Al-Shammari, M. J. , 2015, “ Profiling Downhole Casing Integrity Using Artificial Intelligence,” The SPE Digital Energy Conference and Exhibition, The Woodlands, TX, Mar. 3–5, SPE Paper No. SPE-173422-MS.
Al-Thuwaini, J. , Zangl, G. , and Phelps, R. E. , 2006, “ Innovative Approach to Assist History Matching Using Artificial Intelligence,” Intelligent Energy Conference and Exhibition, Amsterdam, The Netherlands, Apr. 11–13, SPE Paper No. SPE-99882-MS.
Shahkarami, A. , Mohaghegh, S. D. , Gholami, V. , and Haghighat, S. A. , 2014, “ Artificial Intelligence (AI) Assisted History Matching,” SPE Western North American and Rocky Mountain Joint Meeting, Denver, CO, Apr. 17–18, SPE Paper No. SPE-169507-MS.
Saggaf, M. M. , and Nebrija, E. L. , 1998, “ Estimation of Lithologies and Depositional Facies From Wireline Logs,” SEG Annual Meeting, New Orleans, LA, Sept. 13–18, Paper No. SEG-1998-0288. https://library.seg.org/doi/abs/10.1190/1.1820405
Wu, X. , and Nyland, E. , 1986, “ Well Log Data Interpretation Using Artificial Intelligence Technique,” SPWLA 27th Annual Logging Symposium, Houston, TX, June 9–13, Paper No. SPWLA-1986-M. https://www.onepetro.org/conference-paper/SPWLA-1986-M
Moussa, T. , Elkatatny, S. , Mahmoud, M. , and Abdulraheem, A. , 2018, “ Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches,” ASME J. Energy Resour. Technol., 140(7), p. 072903. [CrossRef]
Lim, J.-S. , Kang, J. M. , and Kim, J. , 1998, “ Artificial-Intelligence Approach for Well-to-Well Log Correlation,” SPE India Oil and Gas Conference and Exhibition, New Delhi, India, Feb. 17–19, SPE Paper No. SPE-1198-0030-JPT.
Denney, D. , 1998, “ Artificial-Intelligence Approach for Well-To-Well Log Correlation,” J. Pet. Technol., 50(11), pp. 30–32. [CrossRef]
Wiener, J. , Rogers, J. , and Moll, B. , 1995, “ Predict Permeability From Wireline Logs Using Neural Networks,” Pet. Engineer Int., 68(5), pp. 777–787. https://www.osti.gov/biblio/49297-predict-permeability-from-wireline-logs-using-neural-networks
Abdulhameed, A. , Elkatatny, S. M. , Mahmoud, M. A. , Aburesh, M. , Abdulraheem, A. , and Ali, A. , 2017, “ Determination of the Total Organic Carbon (TOC) Based on Conventional Well Logs Using Artificial Neural Network,” Int. J. Coal Geol., 179, pp. 72–80. [CrossRef]
Allain, O. , and Houze, O. P. , 1992, “ A Practical Artificial Intelligence Application in Well Test Interpretation,” European Petroleum Computer Conference, Stavanger, Norway, May 24–27, SPE Paper No. SPE-24287-MS.
Houze, O. P. , and Allain, O. F. , 1992, “ A Hybrid Artificial Intelligence Approach in Well Test Interpretation,” SPE Annual Technical Conference and Exhibition, Washington, DC, Oct. 4–7, SPE Paper No. SPE-24733-MS.
Ahmadi, R. , Shahrabi, J. , and Aminshahidy, B. , 2017, “ Automatic Well-Testing Model Diagnosis and Parameter Estimation Using Artificial Neural Networks and Design of Experiments,” J. Petrol Explor. Prod. Technol., 7(3), pp. 759–783.
Elkatatny, S. , and Mahmoud, M. , 2018, “ Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique,” Arabian J. Sci. Eng., 43(5), pp. 2491–2500. [CrossRef]
El Ouahed, A. K. , Tiab, D. , Mazouzi, A. , and Jokhio, S. A. , 2003, “ Application of Artificial Intelligence to Characterize Naturally Fractured Reservoirs,” SPE International Improved Oil Recovery Conference in Asia Pacific, Kuala Lumpur, Malaysia, Oct. 20–21, SPE Paper No. SPE-84870-MS.
Kumar, A. , 2012, “ Artificial Neural Network as a Tool for Reservoir Characterization and Its Application in the Petroleum Engineering,” Offshore Technology Conference, Houston, TX, Apr. 30–May 3, Paper No. OTC-22967-MS.
Van, S. , and Chon, B. , 2017, “ Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks,” ASME J. Energy Resour. Technol., 140(3), p. 032906. [CrossRef]
Wang, Y. , and Salehi, S. , 2015, “ Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach,” ASME J. Energy Resour. Technol., 137(6), pp. 62903–62909. [CrossRef]
Graves, A. , Liwicki, M. , Fernández, S. , Bertolami, R. , Bunke, H. , and Schmidh, J. , 2009, “ A Novel Connectionist System for Unconstrained Handwriting Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., 31(5), pp. 855–868. [CrossRef] [PubMed]
Lippmann, R. , 1987, “ An Introduction to Computing With Neural Nets,” IEEE ASSP Mag., 4(2), pp. 4–22. [CrossRef]
Hinton, G. E. , Osindero, S. , and The, Y. W. , 2006, “ A Fast Learning Algorithm for Deep Belief Nets,” Neural Comput., 18(7), pp. 1527–54. [CrossRef] [PubMed]
Niculescu, S. P. , 2003, “ Artificial Neural Networks and Genetic Algorithms in QSAR,” J. Mol. Struct., 622(1–2), pp. 71–83. [CrossRef]
Liew, S. S. , Khalil-Hani, M. , and Bakhteri, R. , 2016, “ An Optimized Second Order Stochastic Learning Algorithm for Neural Network Training,” Neurocomputing, 186, pp. 74–89. [CrossRef]
Storn, R. , and Price, K. , 1997, “ Differential Evolution—A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces,” J. Global Optim., 11(4), pp. 341–359. [CrossRef]
Deng, W. , Yang, X. , Zou, L. , Wang, M. , Liu, Y. , and Li, Y. , 2013, “ An Improved Self-Adaptive Differential Evolution Algorithm and Its Application,” Chemom. Intell. Lab. Syst., 128, pp. 66–76. [CrossRef]
Qin, A. K. , Huang, V. L. , and Suganthan, P. N. , 2009, “ Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization,” IEEE Trans. Evol. Comput., 13(2), pp. 398–417. [CrossRef]
Bingham, E. C. , 1922, Fluidity and Plasticity, McGraw-Hill, New York.
Bird, R. B. , Stewart, W. E. , and Lightfoot, E. N. , 1960, Transport Phenomena, Wiley, New York.
Whittaker, A. , 1985, The EXLOG Series of Petroleum Geology and Engineering: Handbooks Theory and Application of Drilling Fluid Hydraulics, D. Reidel Publishing, Dordrecht, The Netherlands.
Metzner, A. B. , 1956, “ Non-Newtonian Technology: Fluid Mechanics and Transfers,” Advances in Chemical Engineering, Academic Press, New York.

Figures

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. 1

Marsh funnel viscometer [13]

Grahic Jump Location
Fig. 6

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

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)

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In