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Collaborating Authors
Results
Compressibility Factor for Sour Gas Reservoirs
Elsharkawy, Adel M. (Kuwait University) | Elkamel, Ali (Kuwait University)
Abstract This paper presents the initial stage of an effort aimed at developing a new correlation to estimate pseudo critical properties for sour gas when the exact composition is not known. Several mixing rules and gas gravity correlations available in the literature are first evaluated and compared. The evaluation is performed on a large database consisting of more than 2000 samples of sour gas compositions collected worldwide. Several evaluation criteria are used including the average absolute deviation (AAD), the standard deviation (SD), the coefficient of correlation, R, and cross plots and error histograms. The mixing rules include: Kay's mixing rule combined with Wichert-Aziz correlation for the presence of non-hydrocarbons, SSBV mixing rule with Wichert and Aziz, Corredor et al. mixing rule, and Piper et al. mixing rule. These methods, in one form or another, use information on gas composition. Three different other methods that are based on gas gravity alone were also analyzed. These are: Standing, Sutton, and Elsharkawy et al. gas gravity correlations. While the methods based on knowledge of composition showed reasonable accuracy, those based on gas gravity alone showed weak accuracy with low correlation coefficients. A new gas gravity correlation that is based on the fraction of non-hydrocarbons present in the sour gas was proposed. Preliminary results indicate that a good improvement over past gravity correlations was achieved. The compositional correlations, still show, however, better accuracy. Research is still going on to come up with more accurate correlations that are based on only readily available descriptors. Introduction Gas compressibility factor is involved in calculating gas properties such as formation volume factor, density, compressibility, and viscosity. All these properties are necessary in the oil and gas industry for evaluating newly discovered gas reservoirs, calculating initial and gas reserves, predicting future gas production, and designing production tubing and pipelines. The industry standard is to measure gas properties, Pressure-Volume-Temperature (PVT), in laboratory using reservoir samples. The draw back is that these isothermally measured PVT data is applicable at measured pressured and reservoir temperature. Calculation methods such as correlations and equations of state are used to predict properties at other pressures and temperatures. Also, laboratory analyses for PVT behavior are sometimes expensive and time consuming. Correlations, which are used to predict gas compressibility factor, are much easier and faster than equations of state. Sometimes these correlations have comparable accuracy to equations of state. Predicting compressibility factor for sour gases is much more difficult than that of sweet gases. Therefore, several attempts have been made to predict compressibility factor for sweet gases. Wichert and Aziz presented corrections for the presence of hydrogen sulfide and carbon dioxide for determining compressibility factor of sour gases. Because there is no exact method for predicting the PVT behavior of natural gases several approximations have been proposed. The most common method is to use one of the forms of the principle of corresponding states. In this form, gas compressibility factor is expressed as a function of pseudo reduced pressure and temperature (Ppr,Tpr). Standing and Katz (SK) presented a chart for determining gas compressibility factor based on the principle of corresponding states. The SK chart was prepared for binary mixtures of low molecular weight sweet gases. Several mathematical expressions fitting the SK chart, have been proposed to calculate the gas compressibility factor. Evaluation of these methods by Takacs and Elsharkawy et al. concluded that Dranchuk-Abou-Kassem (DK) correlation is the most accurate representation of SK chart.
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
- Reservoir Description and Dynamics > Fluid Characterization > Phase behavior and PVT measurements (1.00)
- Production and Well Operations > Production Chemistry, Metallurgy and Biology > Corrosion inhibition and management (including H2S and CO2) (1.00)
- Health, Safety, Environment & Sustainability > Health > Noise, chemicals, and other workplace hazards (1.00)
Abstract This paper presents a new technique to model the behavior of crude oil and natural gas systems. The proposed technique is using a radial basis function neural network model (RBFNM). The model predicts oil formation volume factor, solution gas- oil ratio, oil viscosity, saturated oil density, undersaturated oil compressibility, and evolved gas gravity. Input data to the RBFNM are reservoir pressure, temperature, stock tank oil gravity, and separator gas gravity. The model is trained using differential PVT analysis of numerous crude oil samples collected from various oil fields. The proposed RBFNM is tested using PVT properties of other samples that has not been used during the training process. Accuracy of the proposed network model to predict PVT properties crude oils and gas systems is compared to the accuracy of numerous published PVT correlations. The physical behavior of the model is also checked against experimentally measured data for the test samples. The results showed that the RBFNM is reliable and has better accuracy than the conventional PVT correlations. The model can be incorporated in reservoir simulators and can also be used to check the accuracy of future dilferential PVT reports. This study also shows that once this model is properly trained it can be used to cut expenses of frequent sampling and laborious differential PVT tests. The RBFN model can also be used to forecast PVT properties needed for reservoir and production engineering calculations such as material balance, reservoir simulation separator design, and vertical performance design. P. 35
- Asia (0.68)
- North America > United States > Texas > Dallas County (0.28)
Abstract This study presents a universal neural network based models for the prediction of PVT properties of crude oil samples obtained from all over the world. The data, on which the network was trained, contains 5200 experimentally obtained PVT data sets of different crude oil and gas mixtures from all over the world. They were collected from major producing oil fields in North and South America, North Sea, South East Asia, Middle East and Africa. This represents the largest data set ever collected to be used in developing PVT models. An additional 234 PVT data sets were used to investigate the effectiveness of the neural network models to predict outputs from inputs that were not used during the training process. The neural network model is able to predict the solution gas-oil-ratio and the oil formation-volume-factor as a function of the bubble-point pressure, the gas relative density, the oil specific gravity, and the reservoir temperature. The neural network models were developed using back propagation with momentum for error minimization to obtain the most accurate PVT models. A detailed comparison between the results predicted by the neural network models and those predicted by other correlations are presented for these crude oil samples. This study shows that artificial neural networks, once successfully trained, are excellent reliable predictive tools for estimating crude oil PVT properties better than available correlations. These neural network PVT models can be easily incorporated into reservoir simulators and production optimization software. Introduction Empirical correlations for predicting reservoir fluid properties have been used in evaluating newly discovered formations, studying fluid recoveries, designing production equipment and surface facilities, planning future production and economics. In 1949, Katz introduced the first correlation to predict oil formation volume factor for Mid-Continent US crudes. Since then, several correlations for the prediction of crude oil properties from various locations worldwide have been presented in the literature. Ideally, the PVT properties such as bubble point pressure, gas-oil ratio, and oil formation volume factor are measured on collected bottom hole samples or recombined surface samples. In some occasions, experimentally measured PVT data are not available because adequate samples cannot be obtained or the production horizon does not warrant the expense of detailed reservoir fluid studies. In these cases, field measured data such as reservoir pressure, temperature, crude oil API gravity and gas specific gravity are used to estimate the PVT properties using these empirical correlations. Local PVT correlations for a particular field or region can also be used to check the accuracy of the PVT report for a given crude from the same field or the region. The accuracy of the well-known empirical PVT correlations such as Standing, Vasquez and Beggs, and Glas and the recently developed ones has been the subject of numerous studies. All these studies indicated that these correlations are not accurate to be generalized to predict crude oil properties from various locations. All the correlations mentioned above were developed using conventional regression methods, which may not give reliable results. Artificial neural networks, on the other hand, were shown to have excellent and reliable predictive capabilities. The objective of this paper is two fold:to develop a universal network model using large collection of crude oil properties representing oils from different oil fields in the world to predict the PVT properties of various crude oil systems; is to compare the accuracy of the neural network model to several published correlations. In recent years, the application of artificial neural networks to petroleum engineering problems has been the subject of much study. P. 619^
- Europe (1.00)
- South America (0.88)
- Asia > Middle East > Israel > Mediterranean Sea (0.44)
- North America > United States > Texas > Dallas County (0.28)
- South America > Brazil > Bahia > Reconcavo Basin > Miranga Field > Ilhas Formation (0.99)
- South America > Brazil > Bahia > Reconcavo Basin > Miranga Field > Gomo Formation (0.99)
- South America > Brazil > Bahia > Reconcavo Basin > Miranga Field > Candeias Formation (0.99)