Abstract
Achieving important and effective reservoir parameters requires a lot of time and cost, and also achieving these devices is sometimes not possible. In this research, a dataset including 565 datapoints collected from published articles have been used. The input data for forecasting oil formation volume factor (OFVF) were solution gas oil ratio (Rs), gas specific gravity (γg), API gravity (API0) (or oil density γo), and temperature (T). We have tried to introduce two hybrid methods multilayer perceptron (MLP) with artificial bee colony (ABC) and firefly (FF) algorithms to predict this parameter and compare their results after extraction. After essential investigations in this study, the results show that MLP-ABC gives the best accuracy for predicting OFVF. For MLP-ABC model OFVF prediction accuracy in terms of RMSE T> API> γg and these results show that the effect of Rs is more than other input variables and the effect of γg is the lowest.
Highlights
Accurate and valuable evaluation of PVT properties is one of the main and most obvious concerns of reservoir engineers for reservoir management and evaluation purposes
One of the different methods of pressure maintenance and enhance oil recovery (EOR) is injecting gas to increase the pressure of certain chemicals inside reservoir [61]
As shown in the figure, based on 565 available data from around the world and input variables from this data, all input parameters have a positive effect on oil formation volume factor (OFVF) prediction, which are as follows: Rs> T> API> γg and these results show that the effect of Rs is more than other input variables and the effect of γg is the lowest
Summary
Accurate and valuable evaluation of PVT properties (pressure, volume and temperature) is one of the main and most obvious concerns of reservoir engineers for reservoir management and evaluation purposes. These properties include determining and obtaining properties of reservoir fluids' physical characteristics such as bubble point pressure (BPP), solution gas oil ratio an (GORs) and oil formation volume factor (OFVF), which are key development [15]. Liquids undergo fundamental changes in temperature and pressure through their production path, and during normal pressure discharge process. The optimal design and success of such processes require an accurate understanding of the liquid phase behaviour of the reservoir
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