Bubble Point Research Articles

Deviations from transient linear flow behavior: A systematic investigation of possible controls on abnormal reservoir signatures

Observed deviations from classical diffusion (specifically transient linear flow, TLF) for Permian Basin Wolfcamp shale oil wells have been historically attributed to reservoir geometric heterogeneity and consequent anomalous diffusion (AD). Recently, we have suggested that the combination of multiphase flow and variable operating conditions could create similar reservoir signatures (as quantified using classic flow-regime identification techniques). However, there are additional factors, not previously investigated, that could be affecting flow-regime identification (ID). This work aims to systematically investigate all possible causes of deviations from classical diffusion behavior. A general, new semi-analytical oil flow solution is proposed that explicitly accounts for multi-phase flow, stress-sensitive reservoir properties, and variable operational conditions. This is achieved through using the combination of modified oil-phase pseudo-pressure and material balance pseudo-time. Additionally, reservoir geometric heterogeneity and AD are incorporated into the exponent of the power-law relationship between rate-normalized oil-phase pseudo-pressure and material balance pseudo-time. A new workflow is also developed to improve flow regime ID while various reservoir complex behaviors are occurring simultaneously. Using the new workflow, a comprehensive investigation into which parameters are primarily causing deviations from TLF is performed in use of simulated cases with inputs that are similar to those used to history-match a Wolfcamp shale well. Further, errors in the determination of reservoir and fracture properties caused by misdiagnosis of the flow regime are analyzed. Our extensive investigations demonstrate that: 1) multi-phase flow, and in particular the appearance of gas flow (producing gas-oil-ratio ramps up) as flowing bottomhole pressure (FBHP) drops below the bubble point pressure, has the biggest impact on the deviation from TLF; 2) even for cases with high producing water-oil-ratio, water production has relatively minor effects; 3) pressure-dependent permeability has relatively small effects, even for cases when well FBHP changes strongly; 4) deviations from TLF in a Wolfcamp shale oil example may be primarily caused by the combination of multiphase flow and variable FBHP, while the AD effect is likely secondary. Importantly, if log-log flow-regime identification plots are not corrected for complex reservoir behaviors, the reservoir flow regime may become distorted, leading to the selection of incorrect models for production analysis and errors in reservoir/fracture properties estimates.

Effect of Pressure and Temperature Change on Undersaturated Oil Viscosity

A study has been done based on data received from one of the southern Iraqi's oil fields, consists of eighty eight experimental points of oil viscosity at known pressure and temperature above the bubble point pressure.
 Fourteen formulas assumed to define the effect of pressure and temperature change on undersaturated oil viscosity. Every formula gives a value of viscosity at any temperature and pressure (above bubble point) depending on a known value of
 viscosity at known pressure and temperature. Depending on the least squares method, the constants of these formulas are calculated.
 A formula appeared to have the minimum average absolute error (4.8 %) with a value of correlation coefficient equal to (0.8996) is selected to be the best formula adopted in this study. The most proper formula has been compared with two correlations (Khan's correlation and Vasquez and Beggs correlation) and it appeared to be better than the both correlations.

New Correlation of Oil Compressibility at Pressures Below Bubble Point For Iraqi Crude Oil

Oil compressibility represents a significant character in reservoir simulation, design of surface facilities and the analysis of well tests, specifically for systems below the bubble point pressure. Oil compressibility is not directly measured in the laboratory. It is usually gained indirectly from experimental data recorded in PVT reports. The relative volume from the flash test is used to calculate oil compressibility at pressures above the bubble point pressure. At pressures below the bubble point, the reservoir behavior is simulated by the differential liberation test. The solution gas-oil ratio and the oil formation volume factor from the differential liberation test are employed in the estimation of oil compressibility at pressures below the bubble point pressure This paper purposes new correlation for calculating isothermal oil compressibility coefficient at and below bubble point pressure. The formulation of oil compressibility correlation is very difficult as it depends on many variables. This property is a function of many variables such as bubble point pressure, reservoir pressure, reservoir temperature, solution gas-oil ratio, oil formation volume factor, stock-tank oil gravity, specific gravity of gas and gas formation volume factor. Standing’s (1), McCain et al. (2) and Al-Jarri's (3) correlations were submitted for testing their validity to evaluate their performance with Iraqi crude oils and to compare their results with the results of the new correlation. The achievement of the new correlations has been done using two hundreds and nine data points from twenty PVT tests that were collected from Southern Iraqi fields. The evaluation of the previous correlations has achieved with graphical and statistical methods. These checking methods show a poor agreement between the observed and the calculated values. The checking methods (graphical and statistical) explain that the new correlation that was achieved in this paper is suitable to calculate oil compressibility below bubble point pressure for Iraqi crude oils.

A New Model for Estimation of Bubble Point Pressure Using a Bayesian Optimized Least Square Gradient Boosting Ensemble

Accurate estimation of crude oil Bubble Point Pressure (Pb) plays a vital rule in the development cycle of an oil field. Bubble point pressure is required in many petroleum engineering calculations such as reserves estimation, material balance, reservoir simulation, production equipment design, and optimization of well performance. Additionally, bubble point pressure is a key input parameter in most oil property correlations. Thus, an error in a bubble point pressure estimate will definitely propagate additional error in the prediction of other oil properties. Accordingly, many bubble point pressure correlations have been developed in the literature. However, they often lack accuracy, especially when applied for global crude oil data, due to the fact that they are either developed using a limited range of independent variables or developed for a specific geographic location (i.e., specific crude oil composition). This research presents a utilization of the state-of-the-art Bayesian optimized Least Square Gradient Boosting Ensemble (LS-Boost) to predict bubble pointpressure as a function of readily available field data. The proposed model was trained on a global crude oil database which contains (4800) experimentally measured, Pressure–Volume–Temperature (PVT) data sets of a diverse collection of crude oil mixtures from different oil fields in the NorthSea, Africa, Asia, Middle East, and South and North America. Furthermore, an independent (775) PVT data set, which was collected from open literature, was used to investigate the effectiveness of the proposed model to predict the bubble point pressure from data that were not used during the model development process. The accuracy of the proposed model was compared to several published correlations (13 in total for both parametric and non-parametric models) as well as two other machine learning techniques, Multi-Layer Perceptron Neural Networks (MPL-ANN) and Support Vector Machines (SVM). The proposed LS-Boost model showed superior performance andremarkably outperformed all bubble point pressure models considered in this study.

Vapor-liquid equilibrium and criticality of CO2 and n-heptane in shale organic pores by the Monte Carlo simulation

Thanks to the application of CO2 enhanced oil recovery (EOR) technology in shale oil production, phase behavior of CO2 and alkanes in nanopores has received considerable attention. Understanding the confinement effect on the vapor–liquid equilibrium and critical state of CO2 and alkanes is imperative to the calculation of phase behavior and prediction of oil recovery. In this work, the vapor–liquid equilibrium of pure CO2 and n-heptane (nC7) and their mixtures in shale organic pores are simulated using the Monte Carlo simulation. Compared to the bulk phase, the density of equilibrium gas is larger due to adsorption while the liquid phase density is lower because of capillary condensation. The influence of the confinement effect on the equilibrium phase composition results in the lower bubble point pressure and higher dew point pressure of the binary system. Changes in the equilibrium phase lead to a decrease in the critical temperature and pressure of single-component fluids and the minimum miscible pressure of binary system in nanopores. We fitted the critical property shift and analyzed the influence of pore size on fluid properties such as equilibrium pressure, composition, critical parameters, density distribution, etc. This study provides better insight into vapor–liquid equilibrium and critical state of CO2 and alkane in shale organic pores.

Study of component proportion effects on flash boiling atomization with ternary-alkane fuel mixtures

When liquid fuel is injected under superheated conditions, it would experience rapid evaporation in the form of flash boiling. Bubbles constantly generated inside liquid can enhance the atomization and evaporation of fuel spray, and further changes spray macroscopic structure. Former studies have shown that the presence of high volatility fuel can significantly prompt the atomization of low volatility fuel. However, the effects of component proportion on the flash boiling atomization of multi-component fuel spray have not been fully understood yet. Moreover, the lack of an appropriate superheated index for multi-component fuel spray hinders the deeper physical insight into their flash boiling behavior. To bridge these gaps, three single-component alkanes (n-pentane, iso-octane, and n-decane) with noticeably various vapor pressures were blended in different mixtures to investigate the influence of component proportion on their flash boiling behavior. Their macroscopic spray cross-patterns were tested under various fuel temperature and ambient pressure using a high-speed planar laser Mie-scattering imaging system. The difference of their spray cross-patterns was analyzed. The results indicated that the component proportion of light-end component (with the highest vapor pressure) dominates the spray flash boiling behavior due to its high likelihood of evaporation. Based on these results, a new superheated index modified from the ratio of ambient pressure and bubble point pressure was proposed to predict their flash boiling behavior. This newly proposed superheated index was found to better elucidate the flash boiling behavior of test fuels.

Machine Learning-Based Improved Pressure–Volume–Temperature Correlations for Black Oil Reservoirs

Abstract Pressure–volume–temperature (PVT) properties of crude oil are considered the most important properties in petroleum engineering applications as they are virtually used in every reservoir and production engineering calculation. Determination of these properties in the laboratory is the most accurate way to obtain a representative value, at the same time, it is very expensive. However, in the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the PVT properties. This study demonstrates the combined use of two machine learning (ML) technique, viz., functional network (FN) coupled with particle swarm optimization (PSO) in predicting the black oil PVT properties such as bubble point pressure (Pb), oil formation volume factor at Pb, and oil viscosity at Pb. This study also proposes new mathematical models derived from the coupled FN-PSO model to estimate these properties. The use of proposed mathematical models does not need any ML engine for the execution. A total of 760 data points collected from the different sources were preprocessed and utilized to build and train the machine learning models. The data utilized covered a wide range of values that are quite reasonable in petroleum engineering applications. The performances of the developed models were tested against the most used empirical correlations. The results showed that the proposed PVT models outperformed previous models by demonstrating an error of up to 2%. The proposed FN-PSO models were also compared with other ML techniques such as an artificial neural network, support vector regression, and adaptive neuro-fuzzy inference system, and the results showed that proposed FN-PSO models outperformed other ML techniques.

Hybrid Ceramic Membranes for the Removal of Pharmaceuticals from Aqueous Solutions.

Layer-by-Layer (LbL) technology was used to coat alumina ceramic membranes with nanosized polyelectrolyte films. The polyelectrolyte chains form a network with nanopores on the ceramic surface and promote the rejection of small molecules such as pharmaceuticals, salts and industrial contaminants, which can otherwise not be eliminated using standard ultrafiltration methods. The properties and performance of newly developed hybrid membranes are in the focus of this investigation. The homogeneity of the applied coating layer was investigated by confocal fluorescence microscopy and scanning transmission electron microscopy (STEM). Properties such as permeability, bubble point, pore size distribution and Zeta potential were determined for both pristine and LbL coated membranes using various laboratory tests. Subsequently, a thorough comparison was drawn. The charging behavior at solid-liquid interface was characterized using streaming potential techniques. The retention potential was monitored by subjecting widely used pharmaceuticals such as diclofenac, ibuprofen and sulfamethoxazol. The results prove a successful elimination of pharmaceutical contaminants, up to 84% from drinking water, by applying a combination of polyelectrolyte multilayers and ceramic membranes.

Study on Test Method of Automatic Stopping Performance of Liquid Stop Membrane for Infusion Set

At present, there is no guidance or standard document to evaluate the performance of liquid stop membrane for infusion set, and it is impossible to conduct quantitative analysis and quality supervision of product performance. In this paper, a method for measuring bubble point pressure of liquid stop membrane is studied, and the correlation between bubble point pressure and the performance of liquid stop membrane is explored. The results of bubble point pressure test are used as the daily control index of liquid stop performance. It provides a practical reference value for the evaluation of liquid stopping performance of liquid stop membrane.

Measurements and modeling of the phase equilibria for the n-hexane + ethanol + dibutyl ether ternary mixture and its dibutyl ether sub-binary mixtures at 94 kPa

Experimental determinations and theoretical calculations of the phase equilibria for the n-hexane + ethanol + dibutyl ether ternary mixture and the n-hexane + dibutyl ether and ethanol + dibutyl ether binary mixtures are reported in this work at 94 kPa and over the isothermal range 332.3 K to 401.8 K. At the explored conditions, the phase equilibria are characterized by vapor liquid equilibria (VLE) which are measured in an all-glass dynamic VLE cell, whereas the theoretical modeling of the experimental VLE data are carried out by applying classical activity coefficients models (Wilson, and NRTL models), UNIQUAC method, and the molecular based statistical associating fluid theory with variable range using a Mie potential (SAFT-VR Mie) equation of state (EoS).Considering the experimental determinations, the VLE exhibits positive deviation from the Raoult’s law and a zeotropic behavior under the isobaric condition and the isothermal range. The thermodynamical trustworthiness of the measured VLE data is verified by standard consistency test for both binary and ternary mixtures. Based on the statistical evaluation of the dew and bubble points, the Wilson model displays the lower deviations for correlating the VLE of binary mixtures and also to predict the ternary mixture, but the SAFT-VR Mie EoS approach demonstrates a superior capability to model the phase equilibrium of the binary mixtures and to predict the ternary mixture. Finally, it is possible to state that the polarization contribution is not necessary for the mixtures explored in this work.

Estimating polystyrene equation of state (EOS) parameters using the cloud and critical points of polystyrene + hydrocarbon mixtures*

AbstractThree polystyrene parameters for the modified Sanchez‐Lacombe equation of state were estimated by performing a correlation of the cloud and critical point data for six well defined polystyrene + hydrocarbon mixtures. The volume shift, a fourth parameter, was adjusted to the polystyrene pressure‐volume‐temperature (PVT) data. A parameterization of the modified Sanchez‐Lacombe equation of state is used to estimate the hydrocarbon parameters given their critical temperature, critical pressure, and acentric factor, but these properties are not available for the polymer. The original polystyrene parameters estimated solely on the pure component PVT data results have difficulty representing the slope and curvature of the cloud points of binary polystyrene + hydrocarbon mixtures. A previous technique for adjusting one of the polystyrene parameters to match the cloud points with the remainder fit to the PVT data results in an improved match over the pure component PVT data alone. Adjusting three polystyrene parameters can better match the cloud points of a binary polystyrene + hydrocarbon mixture, while only increasing the absolute average deviation in pure polystyrene density by 0.07%. The polystyrene parameters obtained by fitting the cloud points were used to correlate the bubble points of polystyrene in other solvents.

English

Based on the theoretical analysis of capillary hysteresis and relative permeability hysteresis and reservoir numerical simulation, a water injection strategy study was conducted for an offshore weak volatile oil fields after long-term depletion development. The results show that if the capillary hysteresis and relative permeability hysteresis are not considered, the recovery prediction results of the subsequent water injection development will be too small and large, respectively, which is inconsistent with the actual situation. Compared with conventional water injection, cyclic water injection can increase the recovery rate by 1.1%. Its oil displacement presents the characteristics of “three zones and two bands” along the direction from injection to production with water enrichment zone, water stagnation and oil drainage band, oil enrichment zone, oil stagnation and gas drainage band and secondary gas generation zone, respectively. To reduce the influence of the secondary gas cap, the water injection strategy after long-term depletion development includes two slugs of enhanced water injection and cyclic water injection. The timing to turn from enhanced water injection to cyclic water injection is when the formation pressure recovers to bubble point pressure. The optimal half-period of the cyclic water injection phase is 2 months. After the oilfield changed from depletion development to water injection development, the decline rate was improved from 20.8 to -12.7%. The double-slug water injection strategy considering the hysteresis effect can improve the recovery factor by 18.3%, and provide a practical basis for the efficient development of weak volatile reservoirs. Key words: Capillary force hysteresis, relative permeability hysteresis, weak volatile reservoir, cyclic water injection, water injection strategy.

Effects of CO2 binary mixtures on pipeline performance for Carbon Capture and Storage

Captured CO2 from the manufacturing industries and power generation sources are not pure and may contain usually some amount of impure components, which affect the flow dynamics of the CO2 in the pipelines. Major component of impure components includes N2, H2, H2S, O2, Ar etc., depending on the capture technology used for the removal of CO2 streams. To design efficient CO2 pipeline transportation systems, it is imperative to understand the effect of these impure components on the flow behaviour. The simulation of the effect of CO2 binary mixture on pipeline performance is carried out in this study. The steady-state flow in pipeline is described by a set of parabolic mass, momentum and energy conservation equations. To solve the set of equations subject to the boundary and the inlet conditions of the pipeline, the non-linear algebraic solver library DNSQE in Fortran is used to study the behaviour of 90 vol% CO2, and 10 vol% single impure component (N2, H2, H2S, O2 and Ar). The phase diagram and pertinent fluid properties are calculated using the Peng-Robinson (PR) equation of state implemented in NIST Reference Fluid Thermodynamics and Transport Properties database program REFPROP. The results reveal that the bubble point curves were lifted up to higher pressures by impure components. The changes in the bubble point would affect the operating conditions of CO2 pipeline. The presence of impure components also changed the properties of the flowing fluid. From the results, H2 shows the most significant impact on the performance of CO2 pipeline as the highest pressure drop by ca. 0.18 bar/km which has smaller density and greater flow velocity against other CO2 streams. The results also show that Ar had the mildest effect followed by O2, N2 and CO. In the case of H2S, the flow parameters are found to be nearly identical to those for pure CO2. The pressure drop for binary mixture contains H2S shows the same value with pure CO2 stream by ca. 0.13 bar/km.

Optimal Inflow Performance Relationship Equation for Horizontal and Deviated Wells in Low-Permeability Reservoirs

After Vogel proposed a dimensionless inflow performance equation, with the rise of the horizontal well production mode, a large number of inflow performance relationship (IPR) equations have emerged. In the productivity analysis of deviated and horizontal wells, the IPR equation proposed by Cheng is mainly used. However, it is still unclear whether these inflow performance models (such as the Cheng, Klins-Majcher, Bendakhlia-Aziz, and Wiggins-Russell-Jennings types) are suitable for productivity evaluations of horizontal and deviated wells in low-permeability reservoirs. In-depth comparisons and analyses have not been carried out, which hinders improvements in the accuracy of the productivity evaluations of horizontal wells in low-permeability reservoirs. In this study, exploratory work was conducted in two areas. First, the linear flow function relationship used in previous studies was improved. Based on the experimental pressure-volume-temperature results, a power exponential flow function model was established according to different intervals greater or less than the bubble point pressure, which was introduced into the subsequent derivation of the inflow performance equation. Second, given the particularity of low-permeability reservoir percolation, considering that the reservoir is a deformation medium, and because of the existence of a threshold pressure gradient in fluid flow, the relationship between permeability and pressure was changed. The starting pressure gradient was introduced into the subsequent establishment of the inflow performance equation. Based on the above two aspects of this work, the dimensionless IPR of single-phase and oil-gas two-phase horizontal wells in a deformed medium reservoir was established by using the equivalent seepage resistance method and complex potential superposition principle. Furthermore, through regression and error analyses of the standard inflow performance data, the correlation coefficients and error distributions of six types of IPR equations applicable to deviated and horizontal wells at different inclination angles were compared. The results show that the IPR equation established in this study features good stability and accuracy and that it can fully reflect the particularity of low-permeability reservoir seepage. It provides the best choice of the IPR between inclined wells and horizontal wells in low-permeability reservoirs. The other types of IPR equations are the Wiggins-Russell-Jennings, Klins-Majcher, Vogel, Fetkovich, Bendakhlia-Aziz, and Harrison equations, listed here in order from good to poor in accuracy.

An integrated approach for predicting asphaltenes precipitation and deposition along wellbores

Deposition of asphaltenes upon precipitation is a main flow assurance concern, which propelled the development of various experimental and modeling techniques to accurately predict its occurrence. This work develops an integrated approach combining thermodynamic and deposition modules with a multiphase flow simulator to simultaneously model asphaltenes precipitation and deposition in wellbores. The Peng-Robinson equation of state and the modified Miller-Flory-Huggins theory are used to calculate the thermodynamic properties of the oil and asphaltenes precipitation, respectively. The deposition module is based on conservation laws for asphaltenes transport and is linked to the flow simulator to account for the impact of asphaltenes deposit layer on the fluid flow. Two case studies of wells producing through their tubing and casing are analyzed. The results show that failing to update the pressure, temperature and velocity profiles to capture the growth of the asphaltenes layer in the wellbore underestimates the asphaltenes layer thickness. In addition, the bubble point and the asphaltenes lower-onset pressures are predicted to occur closer to the surface. Approximately 20% difference in the asphaltenes deposition thickness after 60 days production is calculated between updating and fixed profile scenarios. Applying different flow models revealed the dependency of the deposition behavior onto model choice. For wellbore that produces through casing, asphaltenes deposition suddenly increases at the entrance then gradually decreases as the casing diameter changes. Despite the higher amount of deposited asphaltenes predicted for casing production, the flow cross-sectional area remained higher compared with tubing production. The integrated approach developed in this work successfully captured the field results, while significantly reduced the required computational time.

Prediction of CO2 Minimum Miscibility Pressure Using an Augmented Machine-Learning-Based Model

Summary Minimum miscibility pressure (MMP) is one of the key design parameters for gas injection projects. It is a physical parameter that is a measure of local displacement efficiency while subject to some constraints due to its definition. Also, the MMP value is used to tune compositional models along with proper fluid description constrained with other available basic phase behavior data, such as bubble point pressure and volumetric properties. In general, carbon dioxide (CO2) and hydrocarbon gases are the most common gases used for (or screened for) gas injection processes, and because of recent focus, they are used to screen for the coupling of CO2-sequestration and CO2-enhanced oil recovery (EOR) projects. Because the CO2/oil phase behavior is quite different than the hydrocarbon gas/oil phase behavior, researchers developed specialized correlations for CO2 or CO2-rich streams. Therefore, there is a need for a tool with expanded range capabilities for the estimation of MMP for CO2 gas streams. The only known and widely accepted measurement technique for MMP that is coherent with its formal definition is the use of a slimtube apparatus. However, the use of slimtube restricts the amount of data available, even though there are other alternative techniques presented over the last three decades, which all have various limitations (Dindoruk et al. 2021). Due to some of the complexities highlighted in Dindoruk et al. (2021) and time and resource requirements, there have been a number of correlations developed in the literature using mostly classical regression techniques with relatively sparse data using various combinations of limited input data (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Jaubert et al. 1998; Emera and Sarma 2005; Yuan et al. 2005; Ahmadi et al. 2010; Ahmadi and Johns 2011). In this paper, we present two separate approaches for the calculation of the MMP of an oil for CO2 injection: analytical correlation in which the correlation coefficients were tuned using linear support vector machines (SVMs) (Press et al. 2007; MathWorks 2020; RDocumentation 2020b; Cortes and Vapnik 1995) and using a hybrid method (i.e., superlearner model), which consists of the combination of random forest (RF) regression (Breiman 2001) and the proposed analytical correlation. Both models take the compositional analysis of oils up to heptane plus fraction, molecular weight of oil, and the reservoir temperature as input parameters. Based on statistical and data analysis techniques in combination with the help of corresponding crossplots, we showed that the performance of the final proposed method (hybrid method) is superior to all the leading correlations (Cronquist 1978; Lee 1979; Yellig and Metcalfe 1980; Alston et al. 1985; Glaso 1985; Emera and Sarma 2005; Yuan et al. 2005) and supervised machine-learning (Metcalfe 1982) methods considered in the literature (Altman 1992; Chambers and Hastie 1992; Chapelle and Vapnik 2000; Breiman 2001; Press et al. 2007; MathWorks 2020). The proposed model works for the widest spectrum of MMPs from 1,000 to 4,900 psia, which covers the entire range of oils within the scope of CO2 EOR based on the widely used screening criteria (Taber et al. 1997a, 1997b). NOTE: A correction notice has been issued for this paper and is available under the Supplementary Data section.

Vapor- liquid equilibrium for the n-dodecane + phenol and n-hexadecane + phenol systems at 523 K and 573 K

A continuous flow apparatus was applied to measure the phase equilibrium at 523 K and 573 K. The performance of the apparatus was analysed with the determination of vapor pressures of water at the temperatures (T = 453 K and 473 K). The measured water vapor pressures deviated from the literature values less than 1 %. Vapor pressures of n-dodecane, n-hexadecane and phenol were measured at the temperatures (T = 523–623 K) and, the bubble point pressures of n-dodecane + phenol and n-hexadecane + phenol were measured at the temperatures (T = 523 K and 573 K). The measured vapor pressures of the pure components were compared with the literature values. Relative vapor pressure deviated from the literature value less than 2 % for all the measured vapor pressures. The measured vapor pressures value in this work agreed well with the literature, which indicates that the measurement apparatus and the method can produce good-quality data. The measured bubble point pressures for the n-dodecane + phenol and n-hexadecane + phenol systems were modeled with Peng-Robinson and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equations of state and Non-random Two-liquid (NRTL) activity coefficient model. The measured systems were at first modeled with Peng-Robinson and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equations of state without binary interaction parameters. Additionally, the parameters were regressed to optimize the performance of the models. The NRTL activity coefficient model described the behaviour of the measured and the literature data better than the equations of state. Furthermore, the Peng-Robinson equation of state resulted in better predictions than PC-SAFT equation of state even without binary interaction parameters regression. Both equations of state modeled the phase equilibrium behaviour of the system well. The n-dodecane + phenol system showed azeotropic behaviour.

A pore-network-based upscaling framework for the nanoconfined phase behavior in shale rocks

The presence of extensive nanopores in organic-rich shale introduces unique thermodynamic fluid phase behaviors owing to large pressure differentials across fluid-fluid interfaces and strong fluid-wall interactions. While the nanoconfined phase behavior has been extensively studied in a single nanopore, its manifestation in complex nanopore networks remains poorly understood and rigorously derived macroscopic phase behavior formulations are not yet available. We develop a novel upscaling framework for deriving macroscopic phase behavior formulations in realistic nanopore networks (e.g., obtained from high-resolution digital images of shale samples). The framework employs a generalized phase equilibrium model that explicitly accounts for the impact of capillary pressure and multicomponent adsorption in each pore. Assuming thermodynamic equilibrium across the pore network, macroscopic phase behavior variables for the entire pore network are then derived by integrating the variables from the individual pores. This leads to a macroscopic network-scale phase equilibrium model that naturally accounts for the size- and geometry-dependent nanoconfinement effects of a complex pore structure. Simulated phase behaviors using three multiscale pore networks demonstrate that (1) the phase behavior in a pore network—controlled by the multiscale pore structure—significantly deviates from that in a single nanopore and (2) due to capillary trapping of the liquid phase and competitive adsorption on the pore wall, heavier components tend to reside in smaller pores and suppress the bubble point pressure therein. The upscaled phase behavior model shares the same mathematical structure as that of a standard phase behavior model and can thus be readily incorporated in commercial reservoir simulators. • We develop a novel upscaling framework for the phase behavior in nanopore networks. • Our formulation accounts for capillary pressure and competitive adsorption. • The nanopore networks can represent realistic pore structures of shale rocks. • The network-scale phase behavior is controlled by the multiscale pore structure. • The upscaled model can be readily incorporated in commercial reservoir simulators.

Boiling Point of Five New Sulfur-free Odorants for LPG, 1-Pentyne, Cyclopentene, 1-Hexyne, 2-Hexyne and 1,5-Cyclooctadiene, and Bubble Point Pressures of Binaries with Propane

Boiling Point of Five New Sulfur-free Odorants for LPG, 1-Pentyne, Cyclopentene, 1-Hexyne, 2-Hexyne and 1,5-Cyclooctadiene, and Bubble Point Pressures of Binaries with Propane

Pore-Scale Simulation of Confined Phase Behavior with Pore Size Distribution and Its Effects on Shale Oil Production

Confined phase behavior plays a critical role in predicting production from shale reservoirs. In this work, a pseudo-potential lattice Boltzmann method is applied to directly model the phase equilibrium of fluids in nanopores. First, vapor-liquid equilibrium is simulated by capturing the sudden jump on simulated adsorption isotherms in a capillary tube. In addition, effect of pore size distribution on phase equilibrium is evaluated by using a bundle of capillary tubes of various sizes. Simulated coexistence curves indicate that an effective pore size can be used to account for the effects of pore size distribution on confined phase behavior. With simulated coexistence curves from pore-scale simulation, a modified equation of state is built and applied to model the thermodynamic phase diagram of shale oil. Shifted critical properties and suppressed bubble points are observed when effects of confinement is considered. The compositional simulation shows that both predicted oil and gas production will be higher if the modified equation of state is implemented. Results are compared with those using methods of capillary pressure and critical shift.