Evaluating Constant Volume Depletion Data

Whitson, Curtis Hayes; Torp, Stein Borre

doi:10.2118/10067-ms

Cited by 49 publications

(20 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The procedure suggested by Coats and Smart (1986) to match the laboratory results was followed. Then the developed EOS model for each sample was used to obtain MBO PVT properties at different separator conditions using Whitson and Torp (1983) procedure. The MBO PVT properties include the four functions required for MBO simulation are (R v , R s , B o , and B g ).…”

Section: Eos Approachmentioning

confidence: 99%

“…The MBO properties or extended black oil was introduced for the first time by Spivak and Dixon (1973). Whitson and Torp (1983), formed a set of steps to estimate the MBO properties according to the constant volume depletion (CVD) test data for the gas condensate. Coats, 1985, introduced a different method for gas condensate reservoir using commercial software of EOS PVT and a regression package to fit the PVT data obtained in the laboratory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

Khamis

Fattah

2018

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

Laboratory experiment ideally, is the main method to obtain PVT properties of the oil and gas reservoir fluids. The alternative two methods widely used when laboratory experiments are not available are: equation of state (EOS) and empirical PVT correlations. The EOS requires lots of numerical computations based on identifying the full compositions of the reservoir fluids properties. The measurement and calculation of these properties are very expensive and time consuming. On the other hand, using of PVT correlations which are based on easily measured field data such as reservoir pressure and temperature, and gas and oil density is reliable and more economic. In this work, three artificial intelligence (AI) technique models were developed to predict the oil-gas ratio (R v ) for volatile oil and gas condensate reservoirs. Thirteen actual reservoir fluid samples (five volatile oils and eight gas condensates) covering a wide range of fluid behavior and characteristics were used. Whitson and Torp three parameters EOS were used to generate modified black oil (MBO) PVT properties that were used as a data set for model development. The MBO PVT data points were extracted for each sample using commercial PVT software at five different separator conditions. The nature of the input data was studied showing that data type is clustered. In addition, the correlations between the input parameters were checked. This preprocessed is helpful in selecting the best method to deal with the input parameters that will be fed to the developed models. According to this analysis and since the input parameters have different ranges, normalization of these parameters is vital to improve the accuracy of the models and to get the solution quickly and efficiently. Results showed that taking the log for the input parameters is the best among the other normalization techniques. The AI techniques that have been implemented in this research are; Artificial Neural Network (ANN) models, Functional Networks (FNs) and Support Vector Machines (SVMs). Models developed based on these techniques used 17,941 data points and a ratio of 70% for training, 15% for validation, and 15% for testing. To develop these models, three Matlab codes were written for each tool where the provided input data in excel format were read and prepressed before implementation. Results obtained using these techniques showed that the ANN model predicted R v with an average square correlation coefficient of 0.9999 and an average relative error of 0.15% while FNs predicted R v with an average correlation coefficient of 0.9635 and an average relative error of 27.6%. It was noted that SVMs gave the best results with an average correlation coefficient of 0.9990 and an average relative error of 0.12%. The results concluded that ANN and SVM accurately predicted such data since this type of data are clustered and these two models can handle this kind of data. The newly developed models depend only on easily obtainable parameters in the field and can have varied applications when t...

show abstract

Section: Eos Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

Khamis

Fattah

2018

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…Discrepancies in the two models were adjusted by applying heptane-plus characterization techniques and EOS tuning methods. The heavier components (heptane-plus) have various isomers for the same carbon number components and hence they have different characteristics by the presence of different isomers [10]. The heptaneplus characterization involved splitting into three fractions; C7+, C14+ and C25+ before lumping into groups of all pseudo-components according to their molecular weights.…”

Section: Fluid Characterization and Generation Of Compositional Pvt Tmentioning

confidence: 99%

Parametric Study of Enhanced Condensate Recovery of Gas Condensate Reservoirs using Design of Experiment

Izuwa¹,

Ogbunude²

2016

J Pet Environ Biotechnol

View full text Add to dashboard Cite

Gas condensate reservoirs usually exhibit reduced well productivity because of condensate dropout that occurs below the dew point pressure. Gas recycling has become one of the most favorable methods of improving recovery of condensed liquid. However, understanding the influence of different injection and reservoir parameters on productivity is of great importance when planning a gas recycling scheme. Traditional methods of sensitization during reservoir simulation for gas condensate fields creates the challenge of quick identification of the most critical properties for sensitization, and hence delay of overall simulation project delivery. This work aims at identifying the key variables that influence productivity of a gas condensate reservoir under a gas recycling scheme using the design of experiment approach (DOE). DOE represents a more effective method for computer-enhanced, systematic approach to experimentation, considering all the factors simultaneously. Identification of these parameters will help simulators achieve best optimization targets and also save time and resources during dynamic simulation projects. Furthermore, it will be shown that experimental design can be used to fit responses (condensate/gas production) to mathematical models that will be able to predict outputs for any given combination of variables.

show abstract

“…The detailed procedure can be found in Vardcharragosad (2011). The Walsh-Towler's method (Walsh and Lake 2003) and Whitson-Torp method (Whitson and Torp 1983) are used to analyze the resulting PVT data. This study is based on the reservoir fluid characterization and input data detailed in Appendix A. Precalculation starts by assuming there is 1.0 MMSCF of GasEquivalent inside the PVT cell of study charged with the reservoir fluid of study at dew point conditions.…”

Section: Simulating the Standard (Bo) Pvt Propertiesmentioning

confidence: 99%

Identification of pitfalls in PVT gas condensate modeling using modified black-oil formulations

Vardcharragosad

Duplaa

Ayala

2014

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

A black-oil (BO) PVT model is a fluid characterization formulation that represents multi-component reservoir hydrocarbons as a binary mixture (i.e., two pseudo-components: ''surface gas'' and ''stock tank oil''). The BO PVT model is widely used in the petroleum industry because it is relatively simple compared to fully compositional modeling in which all or most components are independently accounted for. Since computational complexity increases nearly exponentially with number of components used in the characterization, there always remains a strong incentive to embracing the simplified black oil (binary) characterization as long as the fluid phase behavior allows it. When representing a complex system with this simplified model, a number of limitations arising from its simplicity may exist. In this study, these limitations are highlighted by performing phase behavior simulations for a gas condensate fluid. Rigorous calculations of standard (BO) PVT properties (B o , B g , R s , and R v ) of a the gas condensate reservoir of choice are performed through a series of flash calculations at the prescribed reservoir fluid depletion path. The study demonstrates that the BO PVT model violates the species material balance principle as reservoir pressure depletes while conserving overall mass. This violation can lead to significant errors when coupling the BO PVT model with tank material balance-based techniques. The simulation test case indicates that these models will significantly and consistently underestimate oil formation volume factor (B o ) and solution gas oil ratio (R s ) due to the shortcomings of the BO PVT model.

show abstract

Evaluating Constant Volume Depletion Data

Cited by 49 publications

References 9 publications

Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

Estimating oil–gas ratio for volatile oil and gas condensate reservoirs: artificial neural network, support vector machines and functional network approach

Parametric Study of Enhanced Condensate Recovery of Gas Condensate Reservoirs using Design of Experiment

Identification of pitfalls in PVT gas condensate modeling using modified black-oil formulations

Contact Info

Product

Resources

About