Research Papers: Petroleum Engineering

Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches

[+] Author and Article Information
Tamer Moussa

Department of Petroleum Engineering,
King Fahd University of Petroleum and Minerals,
Dhahran 5049, Saudi Arabia
e-mail: g201105270@kfupm.edu.sa

Salaheldin Elkatatny

Department of Petroleum Engineering,
King Fahd University of Petroleum and Minerals,
Dhahran 5049, Saudi Arabia;
Petroleum Department,
Cairo University,
Cairo 12613, Egypt
e-mail: elkatatny@kfupm.edu.sa

Mohamed Mahmoud

Department of Petroleum Engineering,
King Fahd University of Petroleum and Minerals,
Dhahran 5049, Saudi Arabia
e-mail: mmahmoud@kfupm.edu.sa

Abdulazeez Abdulraheem

Department of Petroleum Engineering,
King Fahd University of Petroleum and
Dhahran 5049, Saudi Arabia
e-mail: toazeez@gmail.com

1Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received August 28, 2017; final manuscript received January 3, 2018; published online March 15, 2018. Assoc. Editor: Ray (Zhenhua) Rui.

J. Energy Resour. Technol 140(7), 072903 (Mar 15, 2018) (8 pages) Paper No: JERT-17-1464; doi: 10.1115/1.4039270 History: Received August 28, 2017; Revised January 03, 2018

Permeability is a key parameter related to any hydrocarbon reservoir characterization. Moreover, many petroleum engineering problems cannot be precisely answered without having accurate permeability value. Core analysis and well test techniques are the conventional methods to determine permeability. These methods are time-consuming and very expensive. Therefore, many researches have been introduced to identify the relationship between core permeability and well log data using artificial neural network (ANN). The objective of this research is to develop a new empirical correlation that can be used to determine the reservoir permeability of oil wells from well log data, namely, deep resistivity (RT), bulk density (RHOB), microspherical focused resistivity (RSFL), neutron porosity (NPHI), and gamma ray (GR). A self-adaptive differential evolution integrated with artificial neural network (SaDE-ANN) approach and evolutionary algorithm-based symbolic regression (EASR) techniques were used to develop the correlations based on 743 actual core permeability measurements and well log data. The obtained results showed that the developed correlations using SaDE-ANN models can be used to predict the reservoir permeability from well log data with a high accuracy (the mean square error (MSE) was 0.0638 and the correlation coefficient (CC) was 0.98). SaDE-ANN approach is more accurate than the EASR. The introduced technique and empirical correlations will assist the petroleum engineers to calculate the reservoir permeability as a function of the well log data. This is the first time to implement and apply SaDE-ANN approaches to estimate reservoir permeability from well log data (RSFL, RT, NPHI, RHOB, and GR). Therefore, it is a step forward to eliminate the required lab measurements for core permeability and discover the capabilities of optimization and artificial intelligence models as well as their application in permeability determination. Outcomes of this study could help petroleum engineers to have better understanding of reservoir performance when lab data are not available.

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Balan, B. , Mohaghegh, S. , and Ameri, S. , 1995, “State-of-the-Art in Permeability Determination From Well Log Data—Part 1: A Comparative Study, Model Development,” SPE Eastern Regional Meeting, Morgantown, WV, Sept. 18–20, SPE Paper No. SPE-30978-MS.
Omar, M. I. , and Todd, A. C. , 1993, “Development of New Modified Black Oil Correlations for Malaysian Crudes,” SPE Asia Pacific Oil and Gas Conference, Singapore, Feb. 8–10, SPE Paper No. SPE-25338-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.
Gotawala, D. R. , and Gates, I. D. , 2012, “A Basis for Automated Control of Steam Trap Subcool in SAGD,” SPE J., 17(3), pp. 680–686. [CrossRef]
Wiener, J. , Rogers, J. , and Moll, B. , 1995, “Predict Permeability From Wireline Logs Using Neural Networks,” Pet. Eng. Int., 68(5), pp. 777–787. https://www.osti.gov/biblio/49297
Katz, D. L. , 1942, “Prediction of the Shrinkage of Crude Oils,” Drilling and Production Practice, New York Jan. 1, API Paper No. API-42-137. https://www.onepetro.org/conference-paper/API-42-137
De Ghetto, G. , and Villa, M. , 1994, “Reliability Analysis on PVT Correlations,” European Petroleum Conference, London, Oct. 25–27, SPE Paper No. SPE-28904-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, SEG Paper No. SEG-1998-0288. https://www.onepetro.org/conference-paper/SEG-1998-0288
Rezaei, M. , and Movahed, B. , 2008, “Lithofacies Prediction and Permeability Values Estimation From Conventional Well-Logs Applying Fuzzy Logic—Case Study: Alwyn North Field,” 19th World Petroleum Congress, Madrid, Spain, June 29–July 3, WPC Paper No. WPC-19-2224. https://www.onepetro.org/conference-paper/WPC-19-2224
Ma, Z. , Leung, J. Y. , and Zanon, S. , 2017, “Practical Data Mining and Artificial Neural Network Modeling for Steam-Assisted Gravity Drainage Production Analysis,” ASME J. Energy Resour. Technol., 139(3), p. 032909. [CrossRef]
Jahanbakhshi, R. , and Keshavarzi, R. , 2016, “Intelligent Classifier Approach for Prediction and Sensitivity Analysis of Differential Pipe Sticking: A Comparative Study,” ASME J. Energy Resour. Technol., 138(5), p. 052904. [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), p. 062903. [CrossRef]
Khaksar Manshad, A. , Rostami, H., and Rezael, S. M., 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]
Das, S. K. , 1998, “Vapex: An Efficient Process for the Recovery of Heavy Oil and Bitumen,” SPE J., 3(3), pp. 232–237. [CrossRef]
Graves, A. , Liwicki, M., and Femandez, S., 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 Teh, Y. W. , 2006, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Comput., 18(7), pp. 1527–1554. [CrossRef] [PubMed]
Niculescu, S. P. , 2003, “Artificial Neural Networks and Genetic Algorithms in QSAR,” J. Mol. Struct.: THEOCHEM, 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]
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]
Fogel, D. B. , and Corne, D. W. , 1998, An Introduction to Evolutionary Computation for Biologists Evolutionary Computation: The Fossil Record, IEEE Press, Piscataway, NJ, pp. 19–38.
Koza, J. R. , 2010, “Human-Competitive Results Produced by Genetic Programming,” Genet. Program. Evolvable Mach., 11(3–4), pp. 251–284. [CrossRef]
Schmidt, M. , and Lipson, H. , 2009, “Distilling Free-Form Natural Laws From Experimental Data,” Science, 324(5923), pp. 81–85. [CrossRef] [PubMed]
Nutonian, 2013, “Eureqa [Computer Package],” Nutonian, Boston, MA, accessed Feb. 20, 2018, www.nutonian.com
Slater, M. , Rovira, A., Southern, R., Swapp, D., Zhang, J., Campbell, C., and Levine, M., 2013, “Bystander Responses to a Violent Incident in an Immersive Virtual Environment,” PLoS One, 8(1), p. e52766. [CrossRef] [PubMed]
Pardoe, H. R. , Abbott, D. F. , and Jackson, G. D. , 2013, “Sample Size Estimates for Well-Powered Cross-Sectional Cortical Thickness Studies,” Hum. Brain Mapp., 34(11), pp. 3000–3009. [CrossRef] [PubMed]
Potomkin, M. , Gyrya, V., Aranson, I., and Berlyand, L., 2013, “Collision of Microswimmers in a Viscous Fluid,” Phys. Rev. E, 87(5), p. 053005. [CrossRef]
Makarov, A. , and Eltsov, I. N. , 2012, “Invasion Modeling Assists in the Formation Permeability Evaluation From Resistivity Profiles and Mudcake Thickness (Russian),” PE Russian Oil and Gas Exploration and Production Technical Conference and Exhibition, Moscow, Russia, Oct. 16–18, SPE Paper No. SPE-160584-RU.


Grahic Jump Location
Fig. 3

Regression plot of estimated permeability from SaDE-ANN model and core permeability data

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Fig. 2

Mud invasion relationship with short/deep resistivity from Schlumberger crossplot Rint-13b

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Fig. 1

Relative importance of different well log data with measured permeability

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Fig. 4

Training and validation errors with iterations

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Fig. 5

Regression plot of estimated permeability from eureqa software and core permeability data

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Fig. 6

Permeability profile of well # 01 based on testing data obtained from SaDE-ANN and eureqa models

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Fig. 7

Regression plot of estimated core permeability data of well # 02 obtained from SaDE-ANN model and eureqa

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Fig. 8

Permeability profile of well # 02 based on validation data obtained from SaDE-ANN and eureqa models



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