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Modeling the correlations of crude oil properties based on sensitivity based linear learning method

Selamat, Ali and Olatunji, Sunday Olusanya and Abdul Raheemb, Abdul Azeez and Omatu, Sigeru (2010) Modeling the correlations of crude oil properties based on sensitivity based linear learning method. Engineering Applications of Artificial Intelligence, 24 (2). pp. 686-696. ISSN 0952-1976

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Official URL: http://dx.doi.org/10.1016/j.engappai.2010.10.007


This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crudeoil systems using sensitivity based linear learning method (SBLLM). PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties, such as bubble-point pressure and oil formation volume factor, is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially their instability and inconsistency during predictions. In this paper, a sensitivitybasedlinearlearningmethod (SBLLM) prediction model for PVT properties is presented using three distinct databases while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. In the formulation used, sensitivity analysis coupled with a linear training algorithm for each of the two layers is employed which ensures that the learning curve stabilizes soon and behaves homogenously throughout the entire process operation. In this way, the model will be able to adequately model PVT properties faster with high stability and consistency. Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling.

Item Type:Article
Uncontrolled Keywords:pressure–volume–temperature, bubble-point pressure, crude oil
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:26313
Deposited By: Narimah Nawil
Deposited On:29 Jun 2012 16:09
Last Modified:09 Nov 2018 16:07

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