Determination Of Minimum Miscibility Pressure Research Articles

Experimental and theoretical determination of minimum miscibility pressure of supercritical CO2 and alkanes at nanoconfinement

The minimum miscibility pressure (MMP) is crucial for CO2 Enhanced Oil Recovery (EOR). However, accurately determining MMP within nanoconfinement has been challenging due to intricate molecular interactions and reduced permeability. This study leveraged nanofluidic technology to determine MMP for supercritical CO2-alkanes at the nanoscale by observing the vanishing interface, noting a 5.1 % reduction for CO2-octane at 30 nm versus 1 μm at 80 ℃. It further explored the impact of alkane type and temperature on nanoconfinement, elucidating the underlying mechanisms. Additionally, a theoretical method was developed to estimate MMP ranging from 10 to 1000 nm, aligning well with the experimental results. The findings highlight the lower nanoscale MMP values compared to the bulk phase. This research presents methods for investigating thermodynamic properties under nanoconfinement, providing insights into the abnormal behaviors of fluids at the nanoscale and contributing to EOR.

Prediction of the minimum miscibility pressure for CO2 flooding based on a physical information neural network algorithm

Abstract The minimum miscibility pressure (MMP) is a crucial parameter in assessing the miscibility of CO2 displacement and evaluating the effectiveness of oil displacement. Traditional methods for calculating MMP are intricate and time-consuming, involving numerous related parameters. Therefore, precise and efficient determination of MMP is highly significant in the development of CO2-driven reservoirs. This study first utilized the Pearson correlation coefficient to analyse the correlation factor mechanism of 36 sets of fine-tube experimental data. Subsequently, the physical information neural network prediction model was employed with reservoir temperature, crude oil composition, and injected gas type as input parameters. The PRI state equation and Glaso correlation equation drove the model, with parameter optimization and training conducted under both physical and data driving. The model demonstrates high prediction accuracy and strong generalization ability. Finally, Validation of the model was performed using fine-tube experimental data from 5 other wells, revealing a relatively small relative deviation between calculated and experimental values, with an average coefficient of determination of 0.95 and an average relative error of 4.42%. The prediction accuracy was improved by about 75% compared to other machine learning algorithms. This model holds potential for application in on-site reservoir development, enhancing the measurement accuracy of the minimum miscible pressure of pure CO2 flooding, greatly shortening the design cycle of reservoir development, expediting the process of reservoir development, and providing technical guidance for improving oil and gas recovery and pure CO2 flooding exploration and development.

Determination of Minimum Miscible Pressure and Phase Behavior of CO2–Shale Oil in Nanopores

CO2 injection into shale oil reservoirs has gained widespread attention as it not only enhances oil recovery but also facilitates the geological storage of CO2. Shale oil reservoirs are characterized by numerous nanopores, and the resident fluid exhibits unique phase behavior due to nanoconfinement effects, resulting in the failure of the traditional theoretical model and microfluidic experiment in depicting the minimum miscible pressure (MMP) of CO2–shale oil in nanopores. This study proposes a method for calculating the MMP of CO2–shale oil in nanopores. It combines the three‐phase equilibrium calculation method of gas phase–liquid phase–adsorbed phase with the multiple mixing cell (MMC) method. The method considers capillary pressure and corrects the critical properties influenced by the nano‐limited domain effect. Using this approach, the phase behavior of CO2–shale oil in nanopores and the MMP are investigated. It is found that as the pore radius decreases, the area of the two‐phase region surrounded by the phase envelope of multicomponent fluids gradually shrinks. The smaller the pore radius, the more significant the shrinkage of the two‐phase region. The entire phase envelope and the critical point shift to the left and down on the coordinates, and this phenomenon is more pronounced for pore radii less than 20 nm. The presence of CO2 increases the system’s critical condensation pressure and decreases the critical condensation temperature. The MMP of the 6‐component system of CO2–shale oil for a pore radius of 5 nm in this paper is 63.7 bar at 101°C.

A One-Dimensional Convolutional Neural Network for Fast Predictions of the Oil-CO2 Minimum Miscibility Pressure in Unconventional Reservoirs

Summary Miscible carbon dioxide (CO2) injection has proven to be an effective method of recovering oil from unconventional reservoirs. An accurate and efficient procedure to calculate the oil-CO2 minimum miscibility pressure (MMP) is a crucial subroutine in the successful design of a miscible CO2 injection. However, current numerical methods for the unconventional MMP prediction are very demanding in terms of time and computational costs which result in long runtime with a reservoir simulator. This work proposes to employ a one-dimensional convolutional neural network (1D CNN) to accelerate the unconventional MMP determination process. Over 1,200 unconventional MMP data points are generated using the multiple-mixing-cell (MMC) method coupled with capillarity and confinement effects for training purposes. The data set is first standardized and then processed with principal component analysis (PCA) to avoid overfitting. The performance of the proposed model is evaluated with testing data. By applying the trained model, the unconventional MMP results are almost instantly produced and a coefficient of determination of 0.9862 is achieved with the testing data. Notably, 98.58% of predicting data points lie within 5% absolute relative error. This work demonstrates that the prediction of unconventional MMP can be significantly accelerated, compared with the numerical simulations, by the proposed well-trained deep learning model with a slight impact on the accuracy.

Feasibility of CO2 Injection for Enhance Oil Recovery: A Case Study in the KMJ Layer of HKY Field

Carbondioxide (CO2) injection is a very effective and proven technology for enhanced oil recovery (EOR). Minimum miscibility pressure (MMP) and oil swelling are two important factors of the CO2 gas displacement mechanism that occurs in the reservoir when CO2 injection is applied to enhance oil recovery. In this study, MMP determination between crude oil samples and CO2 gas has been conducted using three methods, i.e., empirical equation, correlations method, and laboratory experiment using slimtube. The determination of the swelling factor was conducted using a PVT cell, where recombined fluid is injected at the reservoir temperature. The MMP value from the empirical equation (2810 psig) is relatively close to the MMP value from the laboratory experiment (2807 psig), with a difference of 3 psig. The swelling test results show that the bubble point pressure and the swelling factor increase from 410 psig to 2200 psig and from 1.0 to 1.442, respectively, as the CO2 gas injection reaches 46.82% mole. Since the fracture pressure of the KMJ Layer in the HKY Field is 2200 psig and the MMP is 2807 psig, only immiscible CO2 flooding can be applied in the field because the CO2 MMP is higher than the fracture pressure.

Determination of Minimum Miscibility Pressure of CO2-Oil System: A Molecular Dynamics Study.

CO2 enhanced oil recovery (CO2-EOR) has become significantly crucial to the petroleum industry, in particular, CO2 miscible flooding can greatly improve the efficiency of EOR. Minimum miscibility pressure (MMP) is a vital factor affecting CO2 flooding, which determines the yield and economic benefit of oil recovery. Therefore, it is important to predict this property for a successful field development plan. In this study, a novel model based on molecular dynamics to determine MMP was developed. The model characterized a miscible state by calculating the ratio of CO2 and crude oil atoms that pass through the initial interface. The whole process was not affected by other external objective factors. We compared our model with several famous empirical correlations, and obtained satisfactory results—the relative errors were 8.53% and 13.71% for the two equations derived from our model. Furthermore, we found the MMPs predicted by different reference materials (i.e., CO2/crude oil) were approximately linear (R2 = 0.955). We also confirmed the linear relationship between MMP and reservoir temperature (TR). The correlation coefficient was about 0.15 MPa/K in the present study.

A New Predictive Method for CO2-Oil Minimum Miscibility Pressure

Gas injection processes are among the effective methods for enhanced oil recovery. Miscible and/or near miscible gas injection processes are among the most widely used enhanced oil recovery techniques. The successful design and implementation of a miscible gas injection project are dependent upon the accurate determination of minimum miscibility pressure (MMP), the pressure above which the displacement process becomes multiple-contact miscible. This paper presents a method to get the characteristic curve of multiple-contact. The curve can illustrate the character in the miscible and/or near miscible gas injection processes. Based on the curve, we suggest a new model to make an accurate prediction for CO2-oil MMP. Unlike the method of characteristic (MOC) theory and the mixing-cell method, which have to find the key tie lines, our method removes the need to locate the key tie lines that in many cases is hard to find a unique set. Moreover, unlike the traditional correlation, our method considers the influence of multiple-contact. The new model combines the multiple-contact process with the main factors (reservoir temperature, oil composition) affecting CO2-oil MMP. This makes it is more practical than the MOC and mixing-cell method, and more accurate than traditional correlation. The method proposed in this paper is used to predict CO2-oil MMP of 5 samples of crude oil in China. The samples come from different oil fields, and the injected gas is pure CO2. The prediction results show that, compared with the slim-tube experiment method, the prediction error of this method for CO2-oil MMP is within 2%.

Determination of Minimum Miscibility Pressure (MMP) using PVTi Software, Eclipse 300 and Empirical Correlations

One of the most important factors through the miscible gas injection process is to determine the Minimum Miscibility Pressure. According to the definition, the minimum miscibility pressure is the minimum pressure at which, at a constant temperature, the oil and gas injected can dissolve together to form a single phase. This pressure is typically abbreviated as MMP. Among the available methods for determining the minimum miscibility pressure, laboratory methods including slim tube test and ascending bubble apparatus test are more widely utilized. Although the mentioned tests have high measurement accuracy, they are very time consuming and expensive. Therefore, the determination of the minimum miscibility pressure is usually done using computational and simulation approaches that also have high accuracy. Conducting PVT tests and determining their MMP using slim tube method was previously performed. In this study, the minimum miscibility pressure of reservoirs was determined by applying three methods of simulation with PVTi software, simulation with Eclipse 300 software and using Empirical Correlations. By comparing the obtained results and the laboratory results, it was revealed that the simulation by Eclipse 300 is regarded as the fastest and most accurate approach.

Minimum miscibility pressure determination in confined nanopores considering pore size distribution of tight/shale formations

This work develops a modified minimum miscibility pressure (MMP) calculation algorithm that couples the effects of pore size distribution, capillarity, and confinement. Also, a volume translated Peng-Robinson equation of state (PR-EOS) is employed in the proposed algorithm to provide a more accurate prediction on phase densities. To calibrate the proposed algorithm, this study uses a real crude oil sample Zhang and Gu (2015) to perform all calculations. The binary interactive parameters (BIPs) of the components in this oil sample are tuned to match the measured oil-CO2 MMP in tight cores by Zhang and Gu (2015). Using the proposed algorithm, the effects of temperature and pore radius on the confined oil-CO2 MMPs are studied in detail. It is found that the oil-CO2 MMP in nanopores decreases with decreasing pore radii. However, the confined MMP becomes almost constant when the pore radius is larger than 10 nm. With an increasing temperature, the confined oil-CO2 MMP first increases to a certain temperature, and then decreases for all the tested pore radii. Hence, there exists a maximum confined MMP for every pore radius, and the maximum confined MMP decreases with a decreasing pore radius. Moreover, when predicting the confined MMP for a real tight reservoir, the pore size distribution is crucial, and the average pore radius is no longer applicable in the calculations because the confined MMP does not change linearly with pore radius. Results show that the confined MMP calculated using the proposed strategy is noticeably lower than the MMP calculated using the average pore radius.

Empirical correlations for prediction of minimum miscible pressure and near-miscible pressure interval for oil and CO2 systems

Although near-miscible CO2 flooding has recently received considerable attention, no criteria are available to predict its applicability to a specific reservoir. Evaluating the viability of near-miscible flooding requires experimental exploration of a specific region in pressure–temperature space. The near-miscible pressure field is bounded on one side in P, T space by the MMP (minimum miscible pressure). This paper provides robust empirical correlations to estimate the MMP for both pure and impure CO2 and for prediction of the near-miscible pressure region for CO2-oil. Many slim tube analyses, lacking high density data points, systematically underestimate the MMP, when the near miscible region is not accounted for. They are based on 147 published data sets that include: slim tube experimental determination of MMP; interfacial tension (IFT) between oil and CO2; concentration of solution gas; and purity of the CO2. This paper is the first to begin a systematic exploration of the pressure-temperature space within which near-miscible effects characterize CO2 floods. Our new correlations provide a basis for identifying and investigating the nature of near miscible effects associated with existing CO2 floods. For a case study of an offshore field we determined that lower and upper pressure boundaries for effective near-miscible flooding, are 0.87 MMP and 1.07 MMP at reservoir temperatures. The proposed model is the first empirical correlation for the prediction of near-miscible pressure region, it will provide the basis for both screening the relative potential of oil reservoirs for economically viable miscible or near-miscible CO2-flooding.

A new correlation for estimation of minimum miscibility pressure (MMP) during hydrocarbon gas injection

The satisfactory recovery of the hydrocarbon gases has made them a reliable choice for gas injection-based enhanced oil recovery (EOR) techniques. The minimum miscibility pressure (MMP) is a pivotal parameter governing the recovery factor during gas injection processes. Therefore, the determination of the authentic MMP is of a crucial importance. Due to the drawback of the experimental techniques (time and cost), empirical correlations are valuable tools in MMP determination. In this study, a multi-gene genetic programming and another software known as LINGO as an optimization tool are applied to offer a dependable MMP formula based on a comprehensive MMP dataset (a total of 108 MMP data). The independent parameters of reservoir temperature, pseudocritical temperature of the injection gas, molecular weight of C5+ components of the reservoir fluid and the intermediate (H2S, CO2, C2–C4)-to-volatile (N2 and C1) ratio are considered as input variables. A comprehensive set of experimental data covers wide span of primary parameters. Furthermore, in order to judge the accuracy of the suggested model and assess the precision and compare the predicted MMP by the current model with those estimated by preexisting correlations, the statistical and graphical error analyses have been employed. Based on the results, the proposed model can estimate MMP of the associated gas with an average absolute relative error of 9.86%. Also, the proposed correlation is more trustworthy and precise than the preexisting models in an extensive spectrum of thermodynamic circumstances. Eventually, the relevancy factor has depicted that the pseudocritical temperature of the injected gas has the most severe role in miscibility achievement.

Determination of minimum miscible pressure in CO2-crude oil system of Changqing Oilfield

Carbon dioxide miscible flooding in low permeability sandstone reservoir can greatly enhance the recovery, the minimum miscible pressure (MMP) is the starting point of miscible flooding. Its determination is very important for oilfield development. In this paper, the internationally widely accepted thin tube experiment method and the bubble riser method are used to compare the minimum miscible pressure. Research results show that the minimum miscibility pressure obtained by slender tube displacement experiment is 22.12 MPa. The minimum miscible pressure measured by the bubbler method is 32.33 MPa, and the error result is large.

CO2 MMP determination on L Reservoir by using CMG simulation and correlations

CO2 minimum miscibility pressure (MMP) is an important parameter in CO2 enhanced oil recovery (CO2-EOR) process. The CO2 injection must be conducted at the pressure equal or greater than MMP. The aim of this study is to determine CO2 MMP by using one-dimensional CMG simulation model and CO2 MMP correlations on L reservoir. To make an accurate simulation model and to choose the most satisfying correlation, it is necessary to validate by comparing the obtained CO2 MMP with experiments in the laboratory on eight type oil from various fields. The result showed that Zhang’s correlation is the best correlation which have -13.9% average percentage of errors while using the CMG simulation model shows the average percentage errors approximately -6.5 %. Using Zhang’s correlation, CO2 MMP on L reservoir is about 3166 psia and from CMG simulation, CO2 MMP equals to 3219 psia.

WITHDRAWN: Determination of the multiple-contact minimum miscibility pressure of CO2/oil system using oil droplet volume measurement method

Abstract Robust determination of the minimum miscible pressure (MMP) of the gas/oil system is essential for successful carbon dioxide enhanced oil recovery (CO2-EOR) and carbon geological storage applications. In this paper, a novel oil droplet volume measurement method (ODVM) is proposed to measure the multiple contact minimum miscibility pressure (MCMMP) and first contact miscibility pressure (FCMP) in the CO2/n-hexadecane (C16H34) and CO2/liquid paraffin systems. The ODVM method is mainly based on the specific quantitative criterion of the rate of decrease of oil droplet volume and was found to be simple and time-saving in measuring the MCMMP and FCMP. The experimental results showed that the MMP values of the CO2/n-hexadecane system were 6.8 MPa, 7.8 MPa, 9.8 MPa, 11.2 MPa and 15.2 MPa at temperatures of 26.9 °C, 37.2 °C, 47.7 °C, 58.2 °C, and 79.6 °C, respectively. The MMP values of the CO2/liquid paraffin system were 6.4 MPa, 9 MPa, 9.8 MPa, 11.8 MPa and 13 MPa at temperatures of 21.5 °C, 40.6 °C, 61.2 °C, and 81.2 °C, respectively. The experimental data show that the measured MMP values of two oil−CO2 systems increase linearly with the temperature. This article is able to provide a new technique for measuring the MMP between CO2 and oil in the field of enhanced oil recovery.

Enhanced Method-of-Characteristics Approach for Determination of Minimum Miscibility Pressure in Displacements with Bifurcated Phase Behavior

Determination of minimum miscibility pressure (MMP) is an imperative component of optimally designing miscible gas injection processes. Semianalytical method of characteristics (MOC) is superior to...

Molecular insight into the miscible mechanism of CO2/C10 in bulk phase and nanoslits

The determination of minimum miscibility pressure (MMP) of CO2/oil in tight oil reservoirs has aroused huge attention from researchers. In this study, we characterized the mass transfer process and determined the MMP of CO2/oil systems in nanoslits through molecular simulation. A bulk phase and five confined nanoslits with the pore size ranging from 2 to 15 nm were examined. It was found that the MMPs in nanoslits were significantly lower than those in bulk phase, thus revealing that rising the probability of molecular interaction is a vital factor affecting the miscible phase. Furthermore, due to the consideration of adsorption layer, the width of nanoslit with the lowest MMP was larger than the reported value, which will bring more uncertainty to the molecular migration. This simulation study highlighted the crucial role of the confinement effect in the miscible process of CO2 flooding, which might facilitate the development of low permeability reservoirs.

Development of minimum tie line length method for determination of minimum miscible pressure in gas injection process

Gas injection process is a very important technology in enhanced oil recovery. Minimum miscible pressure is one of the key parameters in gas injection processes. Various experimental methods such as slim tube are used to measure MMP. These methods are costly and time consuming. Recently computational methods are used in order to achieve a cost-effective and reliable technique to evaluate MMP. In this work, a new methodology has been proposed for determination of MMP using the minimum tie line length method. A real mixing cell model was developed to estimate the MMP, MME and key tie lines. This method is simple, robust, and faster than conventional one-dimensional simulation of slim tube. The new mixing cells method can accurately determine the whole key tie lines to a shift, regardless of the number of injection gas and reservoir fluid components. Unlike other methods of mixing cells, this method automatically corrects dispersion by additional contacts to achieve the low variation domain of tie line slope. Also, the determination and implementation of the minimum miscibility enrichment are investigated.

Determination and evaluation of minimum miscibility pressure using various methods: experimental, visual observation, and simulation

This research proposes a simultaneous technique using various methods to yield the most reliable Minimum Miscibility Pressure (MMP) value. Several methods have been utilized in this study including slim tube test, swelling test, vanishing interfacial tension test, visual observation during swelling test and vanishing interfacial tension test, and simulation. The proposed method may reduce the uncertainty and avoid doubtful MMP. The method can also demonstrate discrepancies among the results. There were two samples used in this study namely Crude Oil AB-5 and Crude Oil AB-4. It showed that for Crude Oil AB-5 the discrepancies among the results from that of the slim tube test were between 3.9% and 10.4% and 0% and 5.9% for the temperature of 60 °C and 66 °C, respectively. The highest discrepancy was shown by the results from the visual observation during vanishing interfacial tension test and the lowest discrepancy was shown by the results from the swelling test. The vanishing interfacial tension test was found to be the fastest method for predicting the MMP. The method also consumed a smaller amount of oil and gas samples for the experiment. The simultaneous method proposed in this study is considered as more proper and exhibits a valuable method for predicting the MMP. This technique has never been found to be performed by previous researchers and accordingly it becomes the strong point of this study to contribute to the global research in the area of MMP determination.

Mechanisms of Mass Exchange Between Light Oiland Petroleum Gas in Low-Permeability Reservoir Development Process

It is shown that associated petroleum gas (APG) and the product of intrastratal transformation of air (by high-pressure air injection technology) are suitable for oil displacement from low-permeability, high-temperature light oil reservoirs. It was found that APG and air are effective gaseous agents miscible with oil, which are similar in oil-displacing properties. The minimum miscibility pressure (MMP) determination data are presented for an Upper Jurassic oil-APG system. The miscibility in the oil-APG system was achieved by the multicontact mechanism of interaction between oil and enriched gas. A new method is proposed for graphical determination of MMP as a function of gas breakthrough due to pressure, and a method for enhancing gas impact efficiency and for achieving miscibility at a pressure below the MMP, which consists in injection of a liquefied gas prefringe or a wide fraction of hydrocarbons, is validated.

New empirical correlations for determination of Minimum Miscibility Pressure (MMP) during N2-contaminated lean gas flooding

In this study, two powerful intelligent algorithms called Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP), are employed to calculate the MMP parameter during N2-flooding. To this end, two mathematical equations with respect to reservoir temperature (TR), molecular weight of heptane plus fraction (MWC7+), average critical temperature of injection gas (Tcm), volatile components (Vol) and intermediate components (Interm) of the crude oil, were developed. By using several statistical quality measures (such as Average Absolute Relative Deviation (AARD%) and Root Mean Square Error (RMSE)) and visual analyses (such as cross plot and contour map diagrams), the superiority of the proposed tools over the studied literature correlations was demonstrated. It was found that the GMDH-based empirically derived correlation is the most accurate model with AARD = 8.33% and RMSE = 3.92). Moreover, frequency analysis shows that, GMDH-based model gives the best performance because of its highest data frequency rate. Based on the results of sensitivity analysis, intermediate fraction of the crude oil is found as the most affecting parameter on the MMP prediction. Finally, our newly proposed GMDH model can be a good nominee for rapid and cheap estimation of MMP during a typical N2 injection project, accurately.