This paper presented a new prediction model for PressureVolume-Temperature (PVT) properties based on the recently introduced learning algorithm called Sensitivity Based Linear Learning Method (SBLLM) for two-layer feedforward neural networks. 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. In this work, we develop Sensitivity Based Linear Learning method prediction model for PVT properties using two distinct databases , while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. Empirical results from simulation show that the newly developed SBLLM based model produced promising results and outperforms others, particularly in terms of stability and consistency of prediction. Index Terms -Sensitivity based linear learning method (SBLLM), Feedforward neural networks, Empirical correlations, PVT properties, Formation volume factor (Bob)' Bubble point pressure (P b ) .
I. INTRODUC TIONCharacterization of reservoir fluids plays a very crucial role in developing a strategy on how to produce and operate a reservoir. Pressure-Volume-Temperature (PVT) Properties are very crucial for geophysics and petroleum engineers, namely for the utilization in material balance calculations, inflow performance calculations, well log analysis , determining reserve estimates and the amount that can be recovered , the flow rate of oil or gas and numerical reservoir simulations. The phase and volumetric behavior of petroleum reservoir fluids is referred to as PVT [1].Among the PVT properties, the bubble-point pressure (P b) and the Oil formation factor (Bob) are the most important, because they are the most essential factors in reservoir and production computations [2]. The more the preciseness of estimating these properties , the better the calculations involved in reservoir simulation , production, and development. See [I, 3-5] for details.Ideally, these properties are determined from laboratory studies on samples collected from the bottom of the wellbore or on the surface. However, such experimental data are very costly to obtain and the accuracy of such is critical and not often known in advance [2,4]. The way out of this constraint has been to use the empirically derived correlations, which 978-1-4244-7003-7/10/$26.00 ©2010 IEEE 77were developed using equation of state (EOS), linear/nonlinear statistical regression, or graphical techniques. Unfortunately, these correlations are constraint by several limitations [I]. To overcome the shortcomings associated with the earlier correlation methods, researchers made use of artificial intelligence based methods foremost of which is the classical artificial neural network (ANN) and its variants. But still, the developed neural networks correlations often do not perform to expect...