A Sensitivity Study of Reservoir Performance Using a Compositional Reservoir Simulator

1972 ◽  
Vol 12 (01) ◽  
pp. 3-12
Author(s):  
Edward T.S. Huang
Keyword(s):  
Limited Range ◽  
Measured Data ◽  
Compositional Variation ◽  
Fluid Sample ◽  
Laboratory Measurements ◽  
Reservoir Performance ◽  
Reservoir Fluid ◽  
Liquid Phases ◽  
Fluid Properties ◽  
Compositional Simulator

Abstract Simulation of isothermal fluid flow in a reservoir using a compositional simulator requires fluid properties that are functions of pressure and properties that are functions of pressure and composition. These properties, i.e., K-values, densities and viscosities of both vapor and liquid phases, are usually obtained from general correlations phases, are usually obtained from general correlations or laboratory measurements of a reservoir fluid sample during a differential-depletion experiment in a PVT cell. prediction of fluid properties of complex mixtures using existing correlations is generally subject to great uncertainties. The laboratory measured data that are generally correlated as functions of pressure have validity only over a limited range of compositional variation. The purposes of this paper were (1) to assess, using a linear compositional simulator, the error introduced into calculated reservoir performance by employing fluids with a given range of uncertainties in their physical properties; and (2) to examine the validity of using the physical data correlated in the compositional simulator as functions of pressure rather than functions of both pressure and composition. The gas cycling process was chosen for illustration because composition changes during this process are large and results are affected more than in a depletion-type process. The hypothetical reservoir fluid system considered in this study was a methane-n-butane-n-decane mixture chosen to simulate a volatile oil system. The results of this investigation show for the particular system studied that:(1)the K-values for particular system studied that:(1)the K-values for the lighter components have the most significant effect on the calculated reservoir performance; and(2)simulations using fluid properties that are equivalent to the data measured during a differential depletion experiment reliably predict reservoir performance even under conditions where significant performance even under conditions where significant variations in reservoir fluid composition occur. Introduction A number of papers have recently been published concerning the development of compositional reservoir simulators-the mathematical models that simulate isothermal flow of multiphase, multicomponent fluids in porous media considering mass transfer effects. These models, which properly describe the distribution of each individual component in both vapor and liquid phases and account for pressure and compositional dependence of K-values, phase densities and viscosities, are more rigorous than the conventional simulators. The latter assumes that the heavy component does not exist in the vapor phase. To use the compositional simulator, it is highly desirable that fluid properties, i.e., K-values, densities and viscosities, as functions of pressure and composition, be available. However, for complex reservoir fluid mixtures, this information is rarely available. These fluid properties are usually calculated from published generalized correlations or obtained from laboratory measurements of a reservoir fluid sample by performing differential depletion experiments in a PVT cell. Prediction of fluid properties of complex mixtures using existing correlations is generally subject to great uncertainty. These errors will certainly have effects on the predicted reservoir performance. These effects may predicted reservoir performance. These effects may even be amplified if all the fluid properties are calculated from correlations. Improvement of the correlation predicted data by adjusting these data to match the limited available experimental values for the system of interest can be make. Yet there is no guarantee that the adjusted data will describe reliable fluid behavior in the region away from the matched points. On the other hand, the laboratory measured data, which are expressed as functions of pressure only, have validity over a limited range of pressure only, have validity over a limited range of compositional variation. When compositions of reservoir fluids vary significantly, the reliability of applying the laboratory measured data in the numerical simulation becomes questionable. SPEJ p. 3

New Downhole-Fluid-Analysis Tool for Improved Reservoir Characterization

2008 ◽  
Vol 11 (06) ◽  
pp. 1107-1116 ◽  
Author(s):  
Chengli Dong ◽  
Michael D. O'Keefe ◽  
Hani Elshahawi ◽  
Mohamed Hashem ◽  
Stephen M. Williams ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrocarbon Composition ◽  
Fluid Composition ◽  
Fluid Sample ◽  
Fluid Analysis ◽  
Reservoir Fluid ◽  
Reservoir Conditions ◽  
Fluid Properties ◽  
Fluid Characterization ◽  
Compositional Grading

Summary Downhole fluid analysis (DFA) has emerged as a key technique for characterizing the distribution of reservoir-fluid properties and determining zonal connectivity across the reservoir. Information from profiling the reservoir fluids enables sealing barriers to be proved and compositional grading to be quantified; this information cannot be obtained from conventional wireline logs. The DFA technique has been based largely on optical spectroscopy, which can provide estimates of filtrate contamination, gas/oil ratio (GOR), pH of formation water, and a hydrocarbon composition in four groups: methane (C1), ethane to pentane (C2-5), hexane and heavier hydrocarbons (C6+), and carbon dioxide (CO2). For single-phase assurance, it is possible to detect gas liberation (bubblepoint) or liquid dropout (dewpoint) while pumping reservoir fluid to the wellbore, before filling a sample bottle. In this paper, a new DFA tool is introduced that substantially increases the accuracy of these measurements. The tool uses a grating spectrometer in combination with a filter-array spectrometer. The range of compositional information is extended from four groups to five groups: C1, ethane (C2), propane to pentane (C3-5), C6+, and CO2. These spectrometers, together with improved compositional algorithms, now make possible a quantitative analysis of reservoir fluid with greater accuracy and repeatability. This accuracy enables comparison of fluid properties between wells for the first time, thus extending the application of fluid profiling from a single-well to a multiwall basis. Field-based fluid characterization is now possible. In addition, a new measurement is introduced--in-situ density of reservoir fluid. Measuring this property downhole at reservoir conditions of pressure and temperature provides important advantages over surface measurements. The density sensor is combined in a package that includes the optical spectrometers and measurements of fluid resistivity, pressure, temperature, and fluorescence that all play a vital role in determining the exact nature of the reservoir fluid. Extensive tests at a pressure/volume/temperature (PVT) laboratory are presented to illustrate sensor response in a large number of live-fluid samples. These tests of known fluid compositions were conducted under pressurized and heated conditions to simulate reservoir conditions. In addition, several field examples are presented to illustrate applicability in different environments. Introduction Reservoir-fluid samples collected at the early stage of exploration and development provide vital information for reservoir evaluation and management. Reservoir-fluid properties, such as hydrocarbon composition, GOR, CO2 content, pH, density, viscosity, and PVT behavior are key inputs for surface-facility design and optimization of production strategies. Formation-tester tools have proved to be an effective way to obtain reservoir-fluid samples for PVT analysis. Conventional reservoir-fluid analysis is conducted in a PVT laboratory, and it usually takes a long time (months) before the results become available. Also, miscible contamination of a fluid sample by drilling-mud filtrate reduces the utility of the sample for subsequent fluid analyses. However, the amount of filtrate contamination can be reduced substantially by use of focused-sampling cleanup introduced recently in the next-generation wireline formation testers (O'Keefe et al. 2008). DFA tools provide results in real time and at reservoir conditions. Current DFA techniques use absorption spectroscopy of reservoir fluids in the visible-to-near-infrared (NIR) range. The formation-fluid spectra are obtained in real time, and fluid composition is derived from the spectra on the basis of C1, C2-5, C6+, and CO2; then, GOR of the fluid is estimated from the derived composition (Betancourt et al. 2004; Fujisawa et al. 2002; Dong et al. 2006; Elshahawi et al. 2004; Fujisawa et al. 2008; Mullins et al. 2001; Smits et al. 1995). Additionally, from the differences in absorption spectrum between reservoir fluid and filtrate of oil-based mud (OBM) or water-based mud (WBM), fluid-sample contamination from the drilling fluid is estimated (Mullins et al. 2000; Fadnes et al. 2001). With the DFA technique, reservoir-fluid samples are analyzed before they are taken, and the quality of fluid samples is improved substantially. The sampling process is optimized in terms of where and when to sample and how many samples to take. Reservoir-fluid characterization from fluid-profiling methods often reveals fluid compositional grading in different zones, and it also helps to identify reservoir compartmentalization (Venkataramanan et al. 2008). A next-generation tool has been developed to improve the DFA technique. This DFA tool includes new hardware that provides more-accurate and -detailed spectra, compared to the current DFA tools, and includes new methods of deriving fluid composition and GOR from optical spectroscopy. Furthermore, the new DFA tool includes a vibrating sensor for direct measurement of fluid density and, in certain environments, viscosity. The new DFA tool provides reservoir-fluid characterization that is significantly more accurate and comprehensive compared to the current DFA technology.

scholarly journals Effect of Compositional Grading On reservoir Performance

1996 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
M. Al-Rumhy ◽  
A. Al-Bemani ◽  
F. Boukadi
Keyword(s):  
Oil Recovery ◽  
Simulation Analysis ◽  
Fluid Composition ◽  
Fluid Viscosity ◽  
Reservoir Performance ◽  
Reservoir Fluid ◽  
Stock Tank ◽  
Fluid Properties ◽  
Secondary Oil Recovery ◽  
Compositional Grading

In reservoirs with thickness exceeding fifty meters, compositional guiding has been found to cause significant variation in performance. Main fluid properties, governing the magnitude of reservoir performance, such as density; formation volume factor and fluid viscosity experience variation due to varying fluid composition along the hydrocarbon column. These variations cause erroneous estimation of stock-tank oil in place and may infer reservoir engineers to consider inappropriate secondary oil recovery methods, for example. In the presence of gravity segregation within the oil column, heavy ends will form a heavy oil blanket in the lower part of the reservoir. Such a scenario may result in poor displacement and an earlier breakthrough when water drive is the dominant fluid flow mechanism. In this paper reservoir performance due to varying reservoir fluid composition has been examined using  reservoir simulation analysis and recommendations for better characterization of reservoir fluid sampling are outlined.

Effect of Dilution on Acoustic and Transport Properties of Reservoir Fluid Systems and Their Interplay

SPE Journal ◽  
2020 ◽  
Vol 25 (06) ◽  
pp. 2867-2880
Author(s):  
Ram R. Ratnakar ◽  
Edward J. Lewis ◽  
Birol Dindoruk
Keyword(s):  
Measurement Techniques ◽  
Saturation Pressure ◽  
Ultrasonic Pulse ◽  
Acoustic Velocity ◽  
Viscosity Data ◽  
Reservoir Fluid ◽  
Fluid Systems ◽  
Fluid Properties ◽  
Volume Measurements ◽  
Live Oil

Summary Acoustic velocity is one of the key thermodynamic properties that can supplement phase behavior or pressure/volume/temperature (PVT) measurements of pure substances and mixtures. Several important fluid properties are relatively difficult to obtain through traditional measurement techniques, correlations, or equation of state (EOS) models. Acoustic measurements offer a simpler method to obtain some of these properties. In this work, we used an experimental method based on ultrasonic pulse-echo measurements in a high-pressure/high-temperature (HP/HT) cell to estimate acoustic velocity in fluid mixtures. We used this technique to estimate related key PVT parameters (such as compressibility), thereby bridging gaps in essential data. In particular, the effect of dilution with methane (CH4) and carbon dioxide (CO2) at pressures from 15 to 62 MPa and temperatures from 313 to 344 K is studied for two reservoir fluid systems to capture the effect of the gas/oil ratio (GOR) and density variations on measured viscosity and acoustic velocity. Correlative analysis of the acoustic velocity and viscosity data were then performed to develop an empirical correlation that is a function of GOR. Such a correlation can be useful for improving the interpretation of the sonic velocity response and the calibration of viscosity changes when areal fluid properties vary with GOR, especially in disequilibrium systems. In addition, under isothermal conditions, the acoustic velocity of a live oil decreases monotonically with decreasing pressure until the saturation point where the trend is reversed. This observation can also be used as a technique to estimate the saturation pressure of a live oil or as a byproduct of the target experiments. It supplements the classical pressure/volume measurements to determine the bubblepoint pressure.

scholarly journals Reservoir Performance Prediction using Integrated Production Modelling (MBAL Software)

2019 ◽  
Vol 8 (4) ◽  
pp. 1484-1489
Keyword(s):  
Performance Prediction ◽  
History Matching ◽  
Material Balance ◽  
Development Planning ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Production Operation ◽  
Gas Field ◽  
Reservoir Performance ◽  
Fluid Properties

Reservoir performance prediction is important aspect of the oil & gas field development planning and reserves estimation which depicts the behavior of the reservoir in the future. Reservoir production success is dependent on precise illustration of reservoir rock properties, reservoir fluid properties, rock-fluid properties and reservoir flow performance. Petroleum engineers must have sound knowledge of the reservoir attributes, production operation optimization and more significant, to develop an analytical model that will adequately describe the physical processes which take place in the reservoir. Reservoir performance prediction based on material balance equation which is described by Several Authors such as Muskat, Craft and Hawkins, Tarner’s, Havlena & odeh, Tracy’s and Schilthuis. This paper compares estimation of reserve using dynamic simulation in MBAL software and predictive material balance method after history matching of both of this model. Results from this paper shows functionality of MBAL in terms of history matching and performance prediction. This paper objective is to set up the basic reservoir model, various models and algorithms for each technique are presented and validated with the case studies. Field data collected related to PVT analysis, Production and well data for quality check based on determining inconsistencies between data and physical reality with the help of correlations. Further this paper shows history matching to match original oil in place and aquifer size. In the end conclusion obtained from different plots between various parameters reflect the result in history match data, simulation result and Future performance of the reservoir system and observation of these results represent similar simulation and future prediction plots result.

The Prediction of Volumes, Compressibilities and Thermal Expansion Coefficients of Hydrocarbon Mixtures

1968 ◽  
Vol 8 (02) ◽  
pp. 95-106
Author(s):  
Surjit M. Avasthi ◽  
Harvey T. Kennedy
Keyword(s):  
Thermal Expansion ◽  
Isothermal Compressibility ◽  
Absolute Deviation ◽  
Hydrocarbon Mixtures ◽  
Reservoir Performance ◽  
Thermal Expansion Coefficients ◽  
Isobaric Thermal Expansion ◽  
Volume Curve ◽  
Fluid Properties ◽  
Molal Volumes

Abstract An equation developed for gaseous hydrocarbon mixtures predicts molal volumes with an average absolute deviation of 0.73 percent when applied to 264 natural gas and condensate systems including 2,043 PVT points. Another equation developed for liquid hydrocarbon mixtures predicts molal volumes with an average absolute deviation of 1.12 percent when applied to 346 crude oil systems including 1,759 PVT points. Both equations require composition of the mixture to be expressed as mole fraction of methane through heptanes-plus, hydrogen sulfide, nitrogen and carbon dioxide, together with the characteristics of the heptanes-plus fraction in addition to the temperature and pressure. The equations cover wide ranges of the variables involved, and their accuracy is considerably better Than that of other available methods. The equations were differentiated to allow calculation of the coefficients of isothermal compressibility and isobaric thermal expansion. (In this paper the coefficient of isothermal compressibility and the coefficient of isobaric thermal expansion will be expressed as compressibility and thermal expansion coefficient, respectively.) Equations to calculate these quantities are presented. Introduction Calculations of reservoir performance for petroleum reservoirs require accurate knowledge of the volumetric behavior of hydrocarbon mixtures, both liquid and gaseous. Compressibilities are required in transient fluid flow problems, and thermal expansion coefficients are important in thermal methods of production. An accurate laboratory investigation of the PVT behavior of each reservoir fluid encountered would be costly and time consuming. For this reason various correlations for predicting fluid properties have been developed and recorded and recent literature. Correlations have been presented in the form of graphs, tables and equations. Since an increasing number of studies are being conducted with the aid of electronic computers, recent efforts have been directed toward development of correlations suitable for computer programming. Application of computers permits the use of more complex correlations which otherwise are not feasible. Moreover, methods for predicting reservoir performance, particularly those based on the compositional material balance, depend upon the capability of accurately expressing the molal volumes and other fluid properties as functions of pressure, temperature and composition. The coefficient of isothermal compressibility c is defined by(1) and can be computed from the slope of isothermal specific volume curve for each pressure. The compressibility is a point function and has the dimension of reciprocal pressure. The coefficient of isobaric thermal expansion beta is defined as(2) It is a point function and has the dimension of reciprocal temperature. The thermal expansion coefficient can be obtained from the slope of an isobaric specific volume curve for any temperature. SPEJ P. 95ˆ

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