Bottomhole samples collected in wellbore systems using oil-based muds (OBMs) are likely to be contaminated by medium-to-heavy hydrocarbon fractions present in the OBM. Pressure/volume/temperature (PVT) data measured for a contaminated fluid will not be representative for the clean reservoir fluid, and such PVT data are hence often ignored by the operator, which means loss of a considerable investment. A proper estimate of the representative clean reservoir fluid is essential for reserves estimation and facilities design. Unfortunately, no experimental methods exist for such estimations. It would be valuable for the oil industry to have options for numerical cleaning of OBM-contaminated reservoir fluids and to be able to carry out equation-of-state (EOS) modeling and regression for a contaminated composition in a way that would allow PVT data for a contaminated fluid to be corrected to represent the uncontaminated fluid. This paper describes such a methodology, which is integrated with EOS modeling procedures for numerically cleaned reservoir-fluid compositions. Thanks to this method, PVT data for contaminated samples do not have to be ignored and oil and gas operators can justify investing in PVT analyses for contaminated-fluid samples.
This paper details the process through which the available data can be used. The composition of the reservoir fluid is estimated from the composition of the fluid with a certain content of OBM contaminate, and, contrary to other proposed methods for numerical cleaning, it takes into consideration that the aromaticity of the reservoir fluid may deviate substantially from that of the OBM contaminate. A regression procedure is applied afterward using the available PVT data in order to develop ultimately an EOS model for the clean reservoir fluid. Compositional data and PVT data are presented for a real reservoir fluid contaminated with OBM. Because data are also available for the clean reservoir fluid, it has been possible to verify the validity of the suggested procedure. The numerical cleaning procedure does not require any nonstandard laboratory data, and the given method also is not restricted to any particular brand of OBM or well type.
The paper presents an Equation-of-State (EOS) modeling work carried out for a Middle East reservoir fluid for which gas injection was considered for increasing ultimate recovery. The aim of the work was to develop an EOS model that would accurately reproduce the phase behavior in a reservoir on injection of either a hydrocarbon gas (mix of gas condensate and associated rich gas) or a CO2 rich gas. A single EOS model was developed, which provided a good match of data for both injection gases. This EOS model enables compositional reservoir simulation studies to be carried out comparing and contrasting the recovery from the field with each of the two injection gases.
Extensive PVT data was available and to be matched by a 9-component 'lumped' EOS model. Available data included classical PVT data as well as gas injection (EOR) data including solubility swelling, equilibrium contact and slim tube tests. A major challenge was to develop a model which, in addition to classical PVT data, which can easily be regressed to, also matched slim tube minimum miscibility pressures (MMPs). A multi-component tie-line method was used considering combined vaporizing/condensing drives, and the tie-line MMP was afterwards verified using a cell-to-cell simulator.
Depth gradient simulations indicated that the transition from liquid-like to vapor-like properties in the reservoir did not take place through a sharp gas-oil contact (GOC), but happened continuously in a 'transition zone'. An EOS model neglecting such 'transition zones' or simulating a sharp gas-oil contact may lead to severe misinterpretations in reservoir simulations. A segregation model based on irreversible thermodynamics was used to investigate the influence of an observed vertical temperature gradient on the compositional variation with depth.
Compositional reservoir simulations for fields undergoing gas injection are dependent on an Equation-Of-State (EOS) model that will correctly simulate the reservoir fluid phase behavior independent of concentration of injection gas. Such an EOS model will require extensive Enhanced Oil Recovery (EOR) PVT Experiments. It is important at the start of an EOR PVT project to define what experiments need to be undertaken. Only then will it be possible to define the sample volume needed to carry out all the EOR PVT experiments. The reservoir type will determine whether the samples can be taken from bottom hole or from the separator. Fluid samples at atmospheric conditions also need to be taken to carry out Carbon Number (True Boiling Point) distillation test. Different samples will generally have to be comingled to avoid compositional variations between samples used for different experiments.
Gas injection is most efficient if the reservoir pressure is higher than the minimum miscibility pressure (MMP). Miscibility develops through a critical point and therefore in addition to routine PVT data it is also important to measure EOR PVT data that will provide information about MMP and near critical phase behavior. The MMP is measured in a Slim Tube test while a Solubility Swelling Experiment will give the Px-diagram including the critical conditions and composition. It is also recommended to perform the Equilibrium Contact Mix and Multi Contact Studies to measure properties inside the phase envelope at near critical conditions. These two experiments need to be carefully designed to ensure to get the most out of them in the EOS modeling work.
The paper will outline design of EOR experiments, details to be aware of when carrying out the experiments and key data to be matched by the final EOS model.
The paper presents compositional data and PVT data for a Middle East reservoir fluid with a reservoir temperature of 394 K and reservoir pressure of 287 bar. The PVT data was selected and designed to provide the best possible starting point for developing an EOS model that would accurately reproduce the phase behavior of a reservoir fluid subject to injection of either CO2 or a hydrocarbon gas.
To eliminate the uncertainty from use of default molecular weights and densities for the C7+ hydrocarbon fractions the reservoir fluid composition was analyzed using a True Boiling Point (TBP) analysis. PVT experiments, both routine and gas injection (EOR) experiments, were carried out including solubility swelling, equilibrium and multi contact experiments and slim tube tests. With both injection gases the reservoir fluid shows a combined vaporizing/condensing drive mechanism.
A 9-component EOS model was developed for the volume corrected Peng-Robinson equation of state, which shows a good match of all available data. Two methods were used to predict the vaporizing/condensing MMP; (a) a multi-component tie-line MMP algorithm and (b) a compositional 1D simulator. The CO2 MMP is considerably lower than the reservoir pressure while the MMP seen with the hydrocarbon gas is close to the reservoir pressure.
Sah, Pashupati (Calsep A/S) | Gurdial, Gurdev S. (Core Labs. Malaysia Sdn. Bhd.) | Schou Pedersen, Karen (Calsep A/S) | Izwan, Hairul (Core Labs Malaysia Sdn Bhd) | Ramli, Mohd Fadli (Core Labs. Malaysia Sdn. Bhd.)
Bottom-hole samples collected in well-bore systems using oil-based muds (OBMs) are likely to be contaminated by medium to heavy hydrocarbon fractions present in the OBM. PVT data measured for a contaminated fluid will not be representative for the clean reservoir fluid and such PVT data is hence often ignored by the operator, which means loss of a considerable investment. It would be valuable for the oil industry to have options for numerical cleaning of OBM contaminated reservoir fluids and to be able to carry out Equation of State (EOS) modeling and regression for a contaminated composition in a way that would allow PVT data for a contaminated fluid to be corrected to represent the uncontaminated fluid. This paper describes such a methodology, which is integrated with EOS modeling procedures for numerically cleaned reservoir fluid compositions. Thanks to this methodology PVT data for contaminated samples does not have to be ignored and oil & gas operators can justify investing in PVT analyses for contaminated fluid samples.
The paper details the process through which the available data can be utilized. The composition of the reservoir fluid is estimated from the composition of the fluid with a certain content of OBM contaminate. A regression procedure is afterwards applied using the available PVT data in order to ultimately develop an EOS model for the clean reservoir fluid. Compositional data and PVT data are presented for a real reservoir fluid contaminated with OBM. Since also data is available for the clean reservoir fluid, it has been possible to verify the validity of the suggested procedure. The numerical cleaning procedure does not require any non-standard laboratory data and the given method is also not restricted to any particular brand of OBM or well-type.