Prediction Of Fluid Properties Research Articles

Physics-enhanced machine-learning-based prediction of fluid properties for gas injection – Focus on CO2 injection

Gas injection (GI) pressure-volume-temperature (PVT) laboratory data play an important role in evaluating the performance and efficiency of enhanced oil recovery (EOR) processes, including carbon dioxide (CO2) injection. Although typically there is a large conventional PVT laboratory data set corresponding to hydrocarbon reservoirs, GI laboratory studies are relatively scarce. Performing EOR laboratory studies may be either unnecessary in the case of EOR screening, or unfeasible in the case when reservoir fluid composition at current conditions is different from initial conditions. Given that GI is to be widely evaluated as a potential EOR process and the critical emerging importance of CO2 storage, there is increased demand for time- and cost-effective solutions to predict the outcome of associated GI laboratory experiments.While machine learning (ML) is extensively used to predict black-oil (BO) properties, it is not the case for compositional reservoir properties, including those related to GI. The core challenge that is addressed in this paper consists on using the typically extensive conventional hydrocarbon fluid laboratory data to predict the needed GI fluid properties. We present an ML-based solution that predicts swelling test data from readily available fluid properties such as fluid composition and BO properties. That is, learning from samples with GI laboratory data and predicting GI fluid parameters for the much larger data set. We also extend the data by building a synthetic data set using calibrated equation-of-state (EoS) models for the large number of fluid samples with swelling data. The augmented data set consolidating both laboratory data and calibrated model results is used to improve the predictive capability of the ML model for a larger GI fraction space than that covered by the laboratory experiments. We present the algorithms and analyze the results obtained from applying these algorithms on a uniquely extensive corporatewide database.

Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines

Viscosity is an essential mud property which enables it to transport cuttings out of a well. Determining the real time value of mud viscosity at downhole conditions is complex and till date lacks a precise model. Literature is filled with models that are only applicable at surface conditions with most of the models being artificial neural network (ANN) based. Neural networks have been criticized for their long training time because they rely on a trial-and-error approach to determine their optimal architecture. The few models related to mud viscosity at downhole conditions are based on non-linear regression which are not flexible in nature. However, Multivariate Adaptive Regression Splines (MARS) has at no time been used notwithstanding some of its known benefits over ANN and other regression methods. The focus of this study is to model mud plastic viscosity at downhole conditions with the MARS method using parameters such as temperature, pressure and initial mud viscosity. MARS is a flexible non-parametric splines-based technique that extends linear regression models by including nonlinearities and interactions between predictors. An experimental database comprising 88 and 149 data points culled from literature for oil-based mud (OBM) and water-based mud (WBM) respectively were utilized for developing the models. For each mud type, the most suitable model was determined by choosing the model with the maximum coefficient of determination (R2) value and the least values of mean square error (MSE), root mean square error (RMSE), and generalized cross-validation criterion (GCV). Using these criteria, the R2, MSE and RMSE of the MARS models according to the mud type were found to be 0.64, 104.35, 10.21 and 0.71, 12.8, 3.57 for OBM and WBM respectively while the GCV values were 129.3 and 14.38 for OBM and WBM respectively. Due to lack of mud rheology data at downhole conditions, it was difficult to compare the performance of the developed models with existing models. Parametric importance analysis indicate that temperature has the highest effect on mud viscosity estimation at downhole conditions. The developed models are valid for 20 °C ≤ Temperature ≤315 °C and 0 psi ≤ Pressure ≤40,000 psi. This pioneer work on using MARS in drilling fluid property prediction points out the implementation protocol, the dataset utilized, the relative importance of input variables and the explicit model for estimating mud viscosity at downhole conditions.

A combined clustering/symbolic regression framework for fluid property prediction

Symbolic regression techniques are constantly gaining ground in materials informatics as the machine learning counterpart capable of providing analytical equations exclusively derived from data. When the feature space is unknown, unsupervised learning is incorporated to discover and explore hidden connections between data points and may suggest a regional solution, specific for a group of data. In this work, we develop a Lennard–Jones fluid descriptor based on density and temperature values and investigate the similarity between data corresponding to diffusion coefficients. Descriptions are linked with the aid of clustering algorithms, which lead to fluid groups with similar behavior, bound to physical laws. Keeping in mind that the fluid data space goes over the gas, liquid, and supercritical states, we compare clustering results to this categorization and found that the proposed methods can detect the gas and liquid states, while distinct supercritical region characteristics are discovered, where fluid density and temperature affect the diffusion coefficient in a more complex way. The incorporation of symbolic regression algorithms on each cluster provides an in-depth investigation on fluid behavior, and regional expressions are proposed.

Mineral, thermal and deep groundwater of Hesse, Germany

Abstract. The composition of mineral, thermal or deep groundwaters is of interest for several geotechnical applications, such as drinking-water supply, spas or geothermal energy. Verified and reliable knowledge of temperature, pH, hydrochemical composition and other parameters is crucial to extract fluids with as few technical problems as possible and exploit groundwater reservoirs economically and environmentally sustainably. However, at sites where empirical data are lacking, the correct prediction of fluid properties is often difficult, resulting in considerable economic risks. Here we present the first comprehensive and publicly available database of mineral, thermal and deep groundwaters of Hesse compiled from published and our own data. Presently, it contains 1035 datasets from 560 different springs or wells sampled since 1810. A dataset consists of metadata like location; altitude; depth; rock type or stratigraphic unit; information on the water type; references; physical–chemical parameters; concentrations of major, minor and trace elements; and content of dissolved and free gases as well as isotope data. The dataset allows the evaluation of time series and distribution of groundwater properties both laterally and vertically. We show a simple statistical evaluation based on the five major hydrogeological regions of Hesse. Our database can be used to re-evaluate genesis and circulation of deep groundwaters, to estimate reservoir temperatures with a solution geothermometer, or to assess groundwater ages by means of isotope data. It can also be useful for a first conception of deep geothermal utilization. In the future, an update and extension of the database is intended. The database of mineral, thermal and deep groundwaters of Hesse has been made available by Schäffer et al. (2020) in xlsx and csv file format at TUdatalib, https://doi.org/10.25534/tudatalib-340. The second updated version of the database can be accessed directly at https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2508.2 (last access: 20 October 2021).

A new method for logging identification of fluid properties in tight sandstone gas reservoirs based on gray correlation weight analysis — A case study of the Middle Jurassic Shaximiao Formation on the eastern slope of the Western Sichuan Depression, China

Energy exploration is becoming increasingly complex worldwide, and tight sandstone gas is an important field for the future development of the oil and gas industry. For the reservoir properties of the Shaximiao Gas Reservoir on the eastern slope of the Western Sichuan Depression in the Sichuan Basin, western China, it was found that the low-resistance characteristics of the reservoir complicate the gray characteristics among reservoir fluid property parameters. Some commonly used fluid property identification techniques, such as the flow zone index method, correlation analysis method of logging parameters, and traditional mathematical statistical methods, have poor fluid property evaluation results. Therefore, how to eliminate the influence of the gray features among the reservoir parameters on the identification of reservoir fluid properties and how to accurately identify the reservoir fluid properties are urgent problems that need to be solved. In this paper, we have developed a new method for identifying the fluid properties of tight sandstone reservoirs by combining gray system theory and multivariate statistical theory. This method can perform gray correlation weight analysis on parameters (combined parameters) closely related to fluid properties; furthermore, the logging identification method based on gray correlation weight analysis is used to identify reservoir fluid properties. The results indicate that the gray correlation weight analysis can accurately characterize the gray characteristics of reservoir fluid parameters and that the gray comprehensive correlation weight results are in good agreement with the production status of the studied gas reservoir. We used the method to identify the fluid properties of the target layer in 58 wells in the study area, and the discrimination rate of the model was 86.5%. In addition, the new model was used to predict the reservoir fluid properties of 12 newly drilled wells in the study area and the accuracy of the reservoir fluid property prediction was 91.67%.

Predicting Fluid Properties in the MUFITS Reservoir Simulator with User-Supplied Modules

We present a recent development of the MUFITS reservoir simulator aiming at modelling the transport of fluids whose properties and phase equilibria are calculated in a user-supplied external shared library. Both the explicit correlations and tabulated data for the fluid parameters can be implemented in the library that we name the EoS-module (Equation of State-module). An iterative approach—which, for example, is based on the phase equilibria calculation through the Gibbs energy minimisation (GEM) method, can also be used in the EoS-module. A considerable effort has been undertaken to minimise the number of program procedures exported by the shared library. This should facilitate and ease the usage of the developed software extension by the scientific community. Furthermore, we supplement the article with the source code of two simple EoS-modules that can serve as templates in other modelling and software development efforts. The EoS-modules are also useful for coupling MUFITS with other elaborate software for fluid property prediction. To demonstrate such a possibility, we supplement the article with the source code of a more complicated EoS-module that couples MUFITS with the geochemical code GEMS3K. This module is used in a simple 1-D benchmark study showing the capabilities of MUFITS for modelling reactive transport in porous media.

Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A

The present work investigates the application of Machine Learning and Artificial Neural Networks for tackling the complex issue of transcritical sprays, which are relevant to modern compression-ignition engines. Such conditions imply the departure of the classical thermodynamic perspective of ideal gas or incompressible liquid, necessitating the use of costly and elaborate thermodynamic closures to describe property variation and simulation methods. Machine Learning can assist in several ways in speeding up such calculations, either as a compact, trained thermodynamic model that can be coupled to the flow solver, or as a surrogate predictive tool of spray characteristics. In this work, such applications are demonstrated and their performance is assessed against more traditional approaches. Such applications involve the prediction of macroscopic spray characteristics, for example, the spray penetration over time, or the spray distribution in space and time, and predictions of fluid properties for the thermodynamic states encountered in such applications. Macroscopic characteristics can be adequately predicted by relatively simple network structures, involving just a hidden layer of 3–4 neurons, whereas prediction of thermodynamic states requires several layers of 5–20 neurons each. The results of integrating Artificial Neural Networks in transcritical sprays are rather promising; prediction of thermodynamic properties at pressures greater than 1bar has effectively zero error, yielding simulations indistinguishable from standard tabulated approaches with minimal overhead. When used as a regression method for time-histories either of spray characteristics or spray distributions, the results are within experimental uncertainty of similar experiments, not included in the training dataset.

Predicting Reservoir Fluid Properties from Advanced Mud Gas Data

SummaryIn a recent paper, we published a machine learning method to quantitatively predict reservoir fluid gas/oil ratio (GOR) from advanced mud gas (AMG) data. The significant increase of the model accuracy compared to traditional modeling approaches makes it possible to estimate reservoir fluid GOR based on AMG data while drilling, before the wireline operation. This approach has clear advantages because of early access, low cost, and a continuous reservoir fluid GOR for all reservoir zones. This paper releases further study results to predict other reservoir fluid properties in addition to GOR, which is essential for geo-operations, field development plans, and production optimization.Two approaches were selected to predict other reservoir fluid properties. As illustrated by the reservoir fluid density example, we developed machine learning models for individual reservoir fluid properties for the first approach, similar to the GOR prediction approach in the previous paper. As for the second approach, instead of developing many machine learning models for individual reservoir fluid property, we investigated the essential properties for equation of state (EOS) fluid characterization: C6 and C7+ composition and the molecular weight and density of the C7+ fraction. Once these properties are in place, the entire spectrum of reservoir fluid properties can be calculated with the EOS model.The results of reservoir fluid property prediction are satisfactory with both approaches. The reservoir oil density prediction has a mean average error (MAE) of 0.039 g/cm3. The accuracy is similar to the typical density derived from the pressure gradient from wireline logging data. For the essential fluid properties required for EOS model prediction, the overall accuracy is less than the laboratory measurements but acceptable as the early phase estimations. The reservoir fluid properties predicted from the EOS model are similar to the predictions from individual machine learning models. We applied the field measured AMG data into the reservoir fluid property models and achieved good results, as illustrated by the reservoir fluid density example.The previous paper completed the methodology to predict all reservoir fluid properties based on AMG data. This work paves the way to generate a complete reservoir fluid log for all relevant reservoir fluid properties while drilling. The method has a significant business impact, providing full coverage of reservoir fluid properties along the well path in the early drilling phase. The advantage of providing reservoir fluid properties in all reservoir zones while drilling far outweighs the limitation of somewhat reduced reservoir fluid property accuracy.

Combining intermolecular potentials for the prediction of fluid properties: Two-body and three-body interactions.

The ability to combine intermolecular potentials without loss of information is investigated. Molecular simulation results for both vapor-liquid equilibria and supercritical isochoric heat capacities are reported for different combinations of n-m potentials. The role of both additional cohesion and repulsive terms is determined. The 12-8-6 potential obtained by adding an m = 6 contribution to the 12-8 potential significantly broadens the phase envelope, which remains inside of the 12-6 envelope. In contrast, the 12+9-6 potential that involves an additional n = 9 repulsive contribution lifts the phase envelope above the 12-6 values. The 12-8-6 potential significantly reduces the maximum and minimum observed for the isochoric heat capacity at supercritical conditions. In contrast, the additional repulsion of the 12+9-6 potential has a relatively small influence on the supercritical behavior of the isochoric heat capacity. Significantly, a comparison of vapor-liquid equilibria data for two-body only simulations for Ar, Kr, and Xe indicates that there is very good agreement with the 12-8-6 data. This means that the 12-8-6 potential may provide a useful description of two-body only interactions for the noble gases. The 12+9-8 potential at least partially reproduces vapor-liquid properties of noble gases interacting via two-body plus three-body interactions. In general, the combination of potentials provides a mechanism of simplifying the calculation of two-body and two-body plus three-body interactions.

Probing Multiscale Structure and Dynamics of Waxy Crude Oil by Low-Field NMR, X-ray Scattering, and Optical Microscopy

Wax deposition is one of the major concern for waxy crude oil production and transportation. A better understanding and prediction of fluid properties related to this issue requires knowledge of th...

Impacts of Thermochemical Sulfate Reduction, Oil Cracking, and Gas Mixing on the Petroleum Fluid Phase in the Tazhong Area, Tarim Basin, China

Petroleum fluids in the deep Ordovician reservoirs of the Tarim Basin vary in phase and molecular composition. An improved understanding of the secondary geochemical alteration processes is critical for successful exploration and fluid property prediction. Three oil samples from the Ordovician condensate and oil reservoirs were analyzed using comprehensive 2D gas chromatography/time-of-flight mass spectrometry (GC×GC-TOFMS). Molecular signatures revealed varying levels of diamondoids and organosulfur compounds (OSCs) that were preferentially enriched in the condensate and a gas saturated oil; however, these molecular signatures were not generated through in-reservoir oil cracking or thermochemical sulfate reduction (TSR) as favorable thermal and medium conditions were not available. Severe cracking and TSR occurred in deeper Cambrian source-reservoirs and generated secondary geochemical products including diamondoids, OSCs, and H2S. Such secondary products were carried by dry gases derived for oil cracking that migrated upward through the fault system and filled shallower Ordovician oil reservoirs. The differential secondary gas charge can account for the variable fluid composition and varying phase behavior, i.e. the transition from unsaturated oil to gas saturated oil and then to condensate. Condensates were formed from the dissolution of primary oils due to extra gas mixing.

Similarity law for Widom lines and coexistence lines.

The coexistence line of a fluid separates liquid and gaseous states at subcritical pressures, ending at the critical point. Only recently, it became clear that the supercritical state space can likewise be divided into regions with liquidlike and gaslike properties, separated by an extension to the coexistence line. This crossover line is commonly referred to as the Widom line, and is characterized by large changes in density or enthalpy, manifesting as maxima in the thermodynamic response functions. Thus, a reliable representation of the coexistence line and the Widom line is important for sub- and supercritical applications that depend on an accurate prediction of fluid properties. While it is known for subcritical pressures that nondimensionalization with the respective species critical pressures p_{cr} and temperatures T_{cr} only collapses coexistence line data for simple fluids, this approach is used for Widom lines of all fluids. However, we show here that the Widom line does not adhere to the corresponding states principle, but instead to the extended corresponding states principle. We resolve this problem in two steps. First, we propose a Widom line functional based on the Clapeyron equation and derive an analytical, species specific expression for the only parameter from the Soave-Redlich-Kwong equation of state. This parameter is a function of the acentric factor ω and compares well with experimental data. Second, we introduce the scaled reduced pressure p_{r}^{*} to replace the previously used reduced pressure p_{r}=p/p_{cr}. We show that p_{r}^{*} is a function of the acentric factor only and can thus be readily determined from fluid property tables. It collapses both subcritical coexistence line and supercritical Widom line data over a wide range of species with acentric factors ranging from -0.38 (helium) to 0.34 (water), including alkanes up to n-hexane. By using p_{r}^{*}, the extended corresponding states principle can be applied within corresponding states principle formalism. Furthermore, p_{r}^{*} provides a theoretical foundation to compare Widom lines of different fluids.

A novel multiscale approach for rapid prediction of phase behaviors with consideration of molecular conformations

The conformational distribution of flexible molecules may have a significant influence on its physical properties and phase behaviors. While atomistic molecular simulations naturally include conformational changes, they are often computationally expensive and require separate simulations for each state condition. Conversely, an equation of state (EOS) provides a rapid description for a variety of fluid properties over the whole phase space; however, because of the ignorance of molecular structure, it is difficult to obtain the interaction parameters from first‐principles. Here we propose a multiscale approach for simultaneous predictions of fluid properties and molecular conformational distribution. The PR+COSMOSAC EOS is used to provide the fugacity of chemical species in a mixture. The electrostatic and dispersion interactions in the EOS are determined from quantum‐mechanical solvation calculations and molecular dynamics simulations at a few state points. The conformational transitions are considered as chemical reactions with the equilibrium constant determined from ab initio G4 calculations. With all the EOS parameters obtained from molecular simulations at different scales, subsequent phase equilibrium predictions can be achieved within milliseconds. We validated this approach using 1,2‐dichloroethane (DCE), whose dipole moment varies from 6.34 × 10−30 C·m in the gauche‐form to 0 in the trans‐form. Our results show that this approach provides not only accurate predictions for the vapor–liquid and liquid–liquid equilibrium of DCE‐containing fluids but also quantitative descriptions of conformational distribution of DCE in these systems. This novel method can be very useful for the prediction of thermodynamic properties of fluids with explicit inclusion of molecular conformations. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4047–4054, 2016

Forecasting density, oil formation volume factor and bubble point pressure of crude oil systems based on nonlinear system identification approach

Accurate predictions of fluid properties, such as density, oil formation volume factor and bubble point pressure, are essentials for all reservoir engineering calculations. In this paper, an approach based on nonlinear system identification modeling; Nonlinear ARX (NARX) and Hammerstein-Wiener (HW) predictive model, is proposed for forecasting the pressure/volume/temperature (PVT) properties of crude oil systems. To this end, two datasets; one containing 168 PVT samples from different Iranian oil reservoirs and other a databank containing 755 data from various geographical locations, were employed to construct (i.e. train) and evaluate (i.e. test) the models. Simulation results demonstrate that the proposed NARX and HW models outperform previously employed methods including three types of artificial neural networks models (committee machine, multilayer perceptron and radial basis function), two types of ANFIS models (grid partition and fuzzy c-mean) and several empirical correlations with the smallest prediction error, and that they are reliable models for predicting the oil properties in reservoirs engineering among other soft computing approaches.

A comparison of PC-SAFT and PR-Peneloux equations of state in predicting the compressibility factors of lean gas-carbon dioxide mixtures at high pressures

Cubic equations of state have been long used as an accurate method of predicting the fluid phase behavior. Whereas these equations yield good predictions of fluid properties, due to their inherent limitations, they may not produce accurate enough values of the gas compressibility factor at high pressures. The PC-SAFT equation of state, on the other hand, calculates the gas compressibility factor using expressions that are more flexible. In this paper, the PC-SAFT and PR-Peneloux equations of state are compared for their ability to simulate the experimental gas compressibility factors of lean gas-carbon dioxide mixtures. To this end, the experimental compressibility factors of five different lean gas-carbon dioxide mixtures were predicted at high pressures. Two sets of simulations were performed. In the first set, the binary interaction coefficients fitted for the PR equation of state were used in both models. In the second set of simulations, the interaction coefficients were reasonably tuned, so that the experimental data were best described by the models. For both sets, the PC-SAFT equation predicted more accurate compressibility factors than the PR-Peneloux equation. In fact, for the tuned PC-SAFT equation, the prediction accuracy was comparable to the accuracy of the experimental data.

Incorporating 3C seismic data quantitatively for enhanced geologic detail in an oil sands reservoir

A workflow incorporating converted-wave (PS) data in an integrated quantitative interpretation (QI) was illustrated with a case study in the Canadian oil sands in which multiple data types were available including high-quality 3D multicomponent data, dipole sonic logs, and a multicomponent walk-away vertical seismic profile (VSP). In an area with unconventional rock property behavior and complex fluid distributions, the dipole sonic logs provided the data necessary for robust deterministic rock-physics templates. The VSP was essential in depth-registering the P-wave surface seismic with the PS-wave data and in determining the appropriate phase rotation of each data set. The 3D multicomponent seismic was used to derive a variety of separate and joint attributes incorporating amplitude variation with offset, prestack and poststack inversion and multiattribute processes. Finally, all elements of the workflow were combined in an interactive classification procedure for optimum representation of geology in the seismic volume. Results of the QI workflow with and without the PS data were compared with each other, and ultimately, to blind wells to assess the potential benefits of including PS data. The comparison showed that better prediction of fluid properties, without compromising P-wave data resolution, was possible when PS data were included.

New correlations for prediction of viscosity and density of Egyptian oil reservoirs

Density and viscosity are of the most important governing parameters of the fluid flow, either in the porous media or in pipelines. Ideally, viscosity and density determined experimentally in the laboratory on actual fluid samples taken from the field under study. However, in the absence of experimentally measured data, especially during the prospecting phase, or when only invalid samples are available, one can resort to empirically derived PVT correlations. Correlations are also needed for the calculation of multiphase flowing pressure gradients which occur in pipe. These calculations require the prediction of fluid properties at various pressures and temperatures. Even though laboratory measurements of these properties may be available as a function of pressure, they are usually measured under isothermal conditions. The behavior of these properties as a function of temperature is usually predicted by using empirical correlations. So it is of great importance to use accurate correlations to calculate the crude oil density and viscosity at various operating conditions. During the last decades, several correlations have been developed to estimate density and viscosity of oil at different reservoir conditions. However, these correlations may be useful only in regional geological provinces and may not provide satisfactory results when applied to crude oils from other regions since oil properties differ according to its source, origin and core type .Also, crude oil composition is complex and often undefined. Therefore, based on Egyptian oil reservoirs data; new correlations have been developed for predicting density and viscosity of dead and live crude oil. Validity and accuracy of these correlations have been confirmed by comparing the obtained results of these correlations and other ones with experimental data for Egyptian oil samples. Checking results of these correlations show that correlations developed by this study revealed more accurate results than the literature correlations.

Productivity evaluation of hydraulically fractured gas-condensate reservoirs

This study describes an application of a compositional single well simulator to analyse well tests in gas-condensate reservoirs. An important aspect of this application for gas-condensate well tests is accurate fluid property prediction during the multi-phase flow regime, which occurs in the near-well region. The simulator can also be used to understand the impact of liquid drop-out and fracture flow on well productivity. Hydraulic fracturing improves the economics of wells drilled in tight reservoirs. However, the operation involves a significant amount of expenditure. In recent years this technique has also been used to stimulate gas-condensate reservoirs by creating a flow conduit through the condensate banking near the well. Thus, it is crucial to keep a fracture as small as possible. In practice it has been proved that a short, wide fracture can provide much higher production than the traditionally pursued narrow, long fracture. The workflow in this study contains compositional simulation of a single well in a tight gas-condensate reservoir, which is used to generate transient pressure data for well test analysis and interpretation to predict multi-phase flow behaviour, and to analyse productivity impairment due to condensation. Simulation models were then further modified to study the impact of various hydraulic fractures on the well productivity index (PI), which is defined as the ratio of production rate (constant) divided by the pressure drop across the reservoir. PIs for fractured cases are compared with respect to the non-fractured base case. Streamline simulation of the fractured gas-condensate reservoir was also included in the study to allow visualization of the flow profile in and around the hydraulic fracture.

Prediction of vapor liquid equilibria using COSMOtherm

The COSMO-RS method in its COSMOtherm implementation has been used to predict the vapor liquid equilibria of problem sets 1a and 1b of the first industrial fluid property prediction challenge, i.e. for the mixtures of dimethylether (1) and propene (2) and of nitroethane (1) and propylene glycol monomethyl ether (1-methoxy-2-propanol) (2). Good quality predictions for both systems have been achieved within less than one day of work, using experimental values for the pure compound vapor pressures and predicted activity coefficients for the mixture thermodynamics. In addition to the results reported for the challenge predictions based on predicted pure compound, vapor pressures are reported and discussed in this paper.

EOS tuning to model full field crude oil properties using multiple well fluid PVT analysis

Equations of State (EOS) are increasingly being used to model fluid properties of crude oil and gas reservoirs. This technique offers the advantage of an improved fluid property prediction over conventional black oil models. Once the crude oil or condensate fluid system has been probably characterized, its PVT behavior under a variety of conditions can easily be studied. This description is then used, within a compositional simulator, to study and choose among different scenarios for EOR schemes, such as miscible gas injection for oil reservoirs or liquid recovery under lean gas injection for condensate reservoirs. In this paper, crude oil from a reservoir in the United Arab Emirates (UAE) was characterized using the Peng–Robinson Equation of State (PR-EOS) to arrive at one EOS model that accurately describes the PVT behavior of crude oil produced from the different wells in the reservoir. The multi-sample characterization method is used to arrive at one consistent model for crude oil for the whole reservoir. The fluid samples are first analyzed for consistency to make sure that they are representative of oil produced, and then they are used to obtain parameters for EOS model. The tuning procedure for the EOS is done systematically by matching the volumetric and phase behavior results with laboratory results. Also, a consistent C 7+ pseudo-component split using the Whitson splitting method is used for all samples to arrive at a consistent model for crude oil for the whole reservoir. Results showed a very good match of PVT properties predicted using the EOS model with laboratory tests for this field. These results demonstrate the usefulness of the multi-sample method in providing one EOS model for the crude oil using PVT test results from different wells. The EOS model developed for this particular field may be used in reservoir simulation studies to optimize hydrocarbon recovery.