An Analytical Approach to Consistency Checks of Experimental PVT Data

Imo-Jack, Otavie; Emelle, Chima

doi:10.2118/167560-ms

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…Bagley et al divide the solubility parameter into two parts for interacting systems [36]: one part accounts for the "physical or non-chemical" effect, which is called the volumedependent cohesion parameter and is defined as follows: (3) where δ phy is physical part of solubility parameter (MPa 0.5 ). The other part accounts for the "chemical" effects, which is defined as follows: (4) where δ chem is chemical part of solubility parameter (MPa 0.5 ), is the molar energy of vaporization (kJ/mol), and V is molar volume (m 3 /kmol). Molar energy of vaporization ( ), also known as the (latent) heat of vaporization or heat of evaporation, is the energy (enthalpy) that must be added to a liquid (l), to transform a quantity of that liquid into a gas (g) state.…”

Section: Methodsmentioning

confidence: 99%

“…The quality-assurance/quality-control methods can range from simple graphical techniques to A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT 4 predicting compositional changes under varying pressure and temperature in underground reservoirs, surface separations, production and transportation facilities. In particular, they are critical for reliable and successful compositional reservoir simulation [25].…”

Section: Introductionmentioning

confidence: 99%

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A new approach for quality control of reservoir fluid composition data using live oil solubility parameter

Mohammadi¹,

Dehghani

Dinmohammad

2016

Journal of Molecular Liquids

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new approach for quality control of reservoir fluid composition data using live oil solubility parameter

Mohammadi¹,

Dehghani

Dinmohammad

2016

Journal of Molecular Liquids

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An inclusive consistency check procedure for quality control methods of the black oil laboratory data

Hashemi

Monfaredi

Sedaee

2020

J Petrol Explor Prod Technol

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Invalid PVT data provide high uncertainty in field development studies such as reservoir fluid compartmentalization, reserve estimation, reservoir simulation, production engineering, and surface facility design. Then, consistency check should be conducted to ensure the validity of PVT data and to identify the most representative PVT sample(s). In this paper, preliminary and complementary consistency check of PVT laboratory methods was applied to design a new comprehensive validity check procedure for verification of the reservoir fluid properties. This procedure would be ascertained whether or not the data could be used as the most representative fluid sample in further studies. The data used in this study were collected from seven undersaturated oil reservoirs, and challenges were observed through the 28 full set of available PVT laboratory data. In our study quality checking procedure of PVT samples consists of different methods such as graphical method, recombination and material balance check for well stream composition test, Buckley, modified Wilson and Hoffman plot, Watson characterization factor, Y-function of CCE test, Y-function of DL test, compositional material balance of DL test, overall mass balance for density, Bo and GOR of DL test and density check for separator test. After implementing the applicable proposed checking procedure to detect the validity of laboratory PVT sample, only 9 out of 28 PVT samples were satisfied all the preliminary and complementary check methods. Furthermore, a practical strategy, to select representative reservoir fluid sample, was also provided and discussed based on the consistency check, well condition check and comparison of fluid properties.

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PVT Fluid Characterization and Consistency Check for Retrograde Condensate Reservoir Modeling

2014

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Retrograde gas condensate systems are complex systems as a result of the unique compositional changes that when the reservoir pressure is decreased. Correct selection of Equation of State (EOS) is necessary for proper fluid characterization so that the PVT behavior in the simulation model is a good representation of the reservoir fluid. High quality and accurate PVT data can reduce uncertainty in reservoir fluid properties and set the stage for reservoir engineering modeling whilst improving the technical work on which investment decisions are made. In order to obtain reliable PVT data for effective reservoir modeling, the following steps are essential: Acquisition of sufficient volumes of representative reservoir fluid samples. Proper examination and supervision of all field and laboratory experiments to ensure accuracy, consistency and validity of the resulting PVT analysis results. Results from the PVT experiments are imported into PVT software for validation in order to ascertain a good match between the simulated and experimental data. This process generates the Equation of State model required for material balance and other simulation studies for gas condensate reservoirs. The mass balance test is one of the methods that can be used to validate laboratory PVT data. It is a rigorous test for the evaluation of compositional consistency between feed composition, separator vapor and liquid compositions. Other laboratory PVT data validation means include Mass balance plot, Hoffman plot CVD/CCE comparison plots and Campbell diagrams. These plots serve as data quality assessment methods prior to their use for EOS characterization. PVT validation checks helps to confirm the true content of the fluid as either a rich or lean gas condensate and also confirm the Gas oil ratio of the system. Inaccurate PVT data can give misleading information that could cause a wrong evaluation of hydrocarbon in place. However, when these methods are properly applied it could lead to huge savings for the company as accurate results are obtained from reservoir simulation models which could aid optimization efforts and achieve incremental recovery.

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An Analytical Approach to Consistency Checks of Experimental PVT Data

Cited by 7 publications

References 4 publications

A new approach for quality control of reservoir fluid composition data using live oil solubility parameter

A new approach for quality control of reservoir fluid composition data using live oil solubility parameter

An inclusive consistency check procedure for quality control methods of the black oil laboratory data

PVT Fluid Characterization and Consistency Check for Retrograde Condensate Reservoir Modeling

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