El-Sebakhy, Emad Ahmed and Sheltami, Tarek and Al-Bokhitan, Said Y. and Shaaban, Yasser and Raharja, Putu D. and Khaeruzzaman, Yaman (2007) Support Vector Machines Framework for Predicting the PVT Properties of Crude Oil Systems. 2007 SPE Middle East Oil and Gas Show and Conference.
Full text not available from this repository.Abstract
PVT properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties using regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to both machine learning and data mining techniques to play a major role in both oil and gas industry. Unfortunately, the developed neural networks correlations have some limitations as they were originally developed for certain ranges of reservoir fluid characteristics and geographical area with similar fluid compositions. Accuracy of such correlations is often limited and global correlations are usually less accurate compared to local correlations. Recently, support vector machines have been proposed as a new intelligence framework for both prediction and classification based on both structure risk minimization criterion and soft margin hyperplane. This new framework dealt with kernel neuron functions instead of sigmoid-like ones, which allows projection to higher planes and solves more complex nonlinear problems. It has featured in a wide range of medical and business journals, often with promising results. The objective of this research is to assess the benefit of support vector machines as decision making tools in the field of oil and gas industry. To demonstrate the usefulness of the support vector machines technique in petroleum engineering area, we describe both the steps and the use of support vector machine modeling approach for predicting the PVT properties of crude oil systems. A comparative study will be carried out to compare their performance with the performance of the neural networks, nonlinear regression, and the empirical correlations algorithms. A preliminary results show that the performance of support vector machines will be accurate, reliable, and outperform most of the existing approaches. Future work can be achieved by using this new framework as a modeling approach for solving oil and gas industry problems, such as, permeability and porosity prediction, identify liquid-holdup flow regimes, and other reservoir characterization.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods 600 Technology (Applied Sciences) > 660 Chemical Engineering > 665 Technology of Industrial Oils, Fats, Waxes, Gases |
| Divisions: | Faculty of Engineering & Informatics > Informatics |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 13 Oct 2021 03:50 |
| Last Modified: | 13 Oct 2021 03:50 |
| URI: | https://kc.umn.ac.id/id/eprint/18710 |
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