Development of Fluid Properties Correlation For Malaysian Crude

Crude oil is a complex mixture consist of up to 200 or more different organics compounds and mostly there are hydrocarbon. Each type of crude oils from different field might have different combination and concentration of these compounds. The American Petroleum Institute (API) value of a particular crude is the measure of its properties in terms of specific gravity or density. Higher API value implies less denser crude and vice versa. Each field has its own formation and the composition within a field can be similar or be significantly different. Other than API, crude can be characterized based on other non-wanted elements like sulphur which is regulated and must be removed. The purpose of this project is to develop fluid properties correlation for Malaysian crude. This project will be using a dataset of 93 of PVT data gather from experimental work from previous researchers. There are three characteristic that will be developed in this project, those are bubble point pressure, solution gas oil ratio and oil formation volume factor. The model will be tested using experimental data to show the efficiency of the developed model and comparison will be done to compare new model more suitable for Malaysian crude rather than peviou researcher had done. This project will be used MATLAB software and Microsoft Excel through the method of Group Method of Data Handling (GMDH) . GMDH is a family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models. The dataset were established and analyzed using summed contributions from each variable. Based on the result, this model give better estimation by having lower average relative error compare to the previous one. The model describes and predicts and considered better than other published models for Malaysian crude with minimum error. The model can be used to predict the crude properties as the way out instead of using experimental work. The data are useful as an alternative for experimental works in order to predict the characteristics and can provide guidelines for future modification.