Forecasting Oil Formation Volume Factor for API Gravity Ranges Using Artificial Neural Network

Azubuike I. Irene, Ikiensikimama S. Sunday

Abstract


The Oil Formation Volume Factor (FVF) parameter is a very important fluid property in reservoir engineering computations. Ideally, this property should be obtained from actual measurements. Quite often, this measurement is either not available, or very costly to obtain. In such cases, empirically derived correlations are used in the prediction of this property. This work centers on building an artificial neural network (ANN) model to predict oil formation volume factor for the different API gravity ranges. The new models were developed using combination of 448 published data from the Middle East, Malaysia, Africa, North Sea, Mediterranean basin, Gulf of Persian fields and 1389 data set collected from the Niger Delta Region of Nigeria. The data have been divided into the following four different API gravity classes: heavy oils for API≤21, medium oils for 21<API≤26, blend oils for 26<API≤35 and light oils for API>35. The data set was randomly divided into three parts of which, 60% was used for training, 20% for validation, and 20% for testing for each particular API grade. Both quantitative and qualitative assessments were employed to evaluate the accuracy of the models to the existing empirical correlations. The ANN models outperformed the existing empirical correlations by the statistical parameters used with the best rank and better performance plots.

Key words: Oil formation volume factor; Artificial neural network; Back propagation; Statistical analysis; API gravity ranges


Keywords


Oil formation volume factor; Artificial neural network; Back propagation; Statistical analysis; API gravity ranges

Full Text:

PDF

References


[1] Al-Marhoun, M. A. (1988). PVT Corrélations for Middle East Crude Oils. JPT, 5, 650.

[2] Al-Marhoun, M. A., & Osman, E. A. (2002). Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils. Paper SPE 78592 Presented at the 10th Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC), Abu Dhabi, UAE.

[3] Almehaideb, R. A. (1994). Improved PVT Correlations For UAE Crude Oils. Paper SPE 37691 Presented at the 1997 SPE Middle East Oil Show and Conference, Bahrain, March 15–18. 19. Conference, Oct. 16-19.

[4] Buscema, M. (2002). A Brief Overview and Introduction to Artificial Neural Networks Substance Use & Misuse. Substance Use & Misuse, 37(8-10), 1093-1149.

[5] Demuth H., Beale M., & Hagan, M. (2009). Neural Network Toolbox User’s Guide. Natick: The MathWorks.

[6] De-Ghetto G., Paone F., & Alikhan A. A. (1994). Reliability Analysis on PVT Correlation. Paper SPE 28904 Presented at the European Petroleum Conference in London, U.K, 26-27 October.

[7] Deng, A. D. (2007). Prediction of PVT Oil Properties Using Artificial Neural Network (Mater’s thesis). University of Ibadan, Department of Petroleum Engineering, Ibadan, Nigeria.

[8] Elsharkawy, A. M. (1998). Modeling the Properties of Crude Oil and Gas Systems Using RBF Network. Presented at the SPE Asia Pacific Oil & Gas Conference, Perth, Australia, October 12-14.

[9] Gharbi, R. B., & Elsharkawy, A. M. (1997). Universal Neural-Network Model for Estimating the PVT Properties of Crude Oils. Paper SPE 38099 Presented at the SPE Asia Pacific Oil & Gas Conference, Kuala Lumpur, Malaysia, and April. 14-16.

[10]
Gharbi, R. B., & Elsharkawy, A. M. (1997). Neural-Network Model for Estimating the PVT Properties of Middle East Crude Oils. Paper SPE 37695 Presented at the SPE Middle East Oil Show and Conference, Bahrain, March. 15–18.

[11] Glaso, O. (1980). Generalized Pressure-Volume Temperature Correlations. JPT, 5, 785-795.

[12]
Hagan, M. T., Demuth, H. B., & Beal, M. (1996). Neural Network Design. Boston: PWS Publishing Company.

[13]Ikiensikimama, S. S. (2009). Reservoir Fluid Property Correlations, Advances in Petroleum Engineering, Chi Ikoku Petroleum Engineering Series. Port Harcourt: IPS Publications.

[14] Ikiensikimama, S. S., & Ogboja, O. (2009). Assessment of Bubblepoint Oil Formation Factor Empirical PVT Correlations. Global Journal of Pure and Applied Science, 15(1), 53-59.

[15] Kay, A. (2001). Artifical Neural Networks. Computer World, 35(2).

[16] Moghadassi, A. R., Parvizian, F., Hosseini, S. M., & Fazlali, A. R. (2009). A New Approach for Estimation of PVT Properties of Pure Gases Based on Artificial Neural Network Model. Brazilian Journal of Chemical Engineering Department, Faculty of Engineering, Arak University, 26(1), 199-206.

[17] MATLAB (2004). Artifical Neural Networks.

[18] Obomanu, D. A., & Okpobiri, G. A. (1987). Correlating the PVT Properties of Nigerian Crude. Trans ASME, 109, 214-24.

[19] Omar, M. I., & Todd, A. C. (1993). Development of New Modified Black Oil Correlation for Malaysian Crudes. Paper SPE 25338, Presented at the 1993 SPE Asia Pacific Oil and Gas Conference, Singapore.

[20] Omole, O., Falode, O. A., & Deng, A. D. (2009). Prediction of Nigerian Crude Oil Viscosity Using Artificial Neural Network. Petroleum and Coal, 151(3), 181-188.

[21] Osman, E.A, Abdel-Wahhab, O.A, & Al-Marhoun, M. A. (2001). Prediction of Oil Properties Using Neural Networks. SPE Paper 68233 Presented at the SPE Middle East Oil Show Conference, Bahrain.

[22] Petrosky, J., & Farshad, F. (1993). Pressure Volume Temperature Correlation for the Gulf of Mexico. Paper SPE 26644 Presented at the 1993 SPE Annual Technical Conference and Exhibition, Houston, TX.

[23] Standing, M. B. (1947). A Pressure-Volume-Temperature Correlation for Mixtures of California Oils and Gases. Drill & Prod. Pract., API.

[24] Shokir, E. M., Goda, H. M., Sayyouh, M. H, & Fattah, K. A. (2004). Modeling Approach for Predicting PVT Data. Engineering Journal of the University of Qatar.

[25] Sozen, A., Arcakilioglu, E., & Ozalp, M. (2004). Investigation of Thermodynamic Properties of Refrigerant/Absorbent Couples Using Artificial Neural Networks. Chemical Engineering and Processing, 43(10), 1253-1264.




DOI: http://dx.doi.org/10.3968%2Fj.aped.1925543820130501.996

Refbacks

  • There are currently no refbacks.


Reminder

How to do online submission to another Journal?

If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.

We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
caooc@hotmail.com; aped@cscanada.net; aped@cscanada.org

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures
Address: 730, 77e AV, Laval, Quebec, H7V 4A8, Canada

Telephone: 1-514-558 6138
Http://www.cscanada.net
Http://www.cscanada.org
E-mail:office@cscanada.net  office@cscanada.org