Estimation Of Minimum Miscibility Pressure Research Articles

RNN-based CO2 minimum miscibility pressure (MMP) estimation for EOR and CCUS applications

Accurate estimation of minimum miscibility pressure (MMP) is crucial for assessing the efficiency of most miscible and immiscible processes, specifically CO2-based enhanced oil recovery (EOR) methods and Carbon capture utilization and sequestration (CCUS). The experimental procedure for MMP prediction is often time-consuming and costly. On the other hand, the empirical models that have been historically used could work based on limited input parameters, ignore the importance of others and are not necessarily accurate. The novelty of the current study is using an explainable deep-learning approach, Recurrent neural network (RNN), to train a model using a multi-dimensional (22 features) dataset with 544 rows of data. The Dataset comprises mole fractions of injected gas (pure and impure CO2). Out of those features, eight subsets of parameters (labelled X_1 to X_8) were used to develop models. The multi-dimensionality of the dataset makes it suitable to study the effects of various parameters on MMP, specifically in conditions of interest to CCUS-EOR applications. Among the multiple inputs tested, the model trained with X_1 and X_8 input parameters (including mole fraction of different hydrocarbon and nonhydrocarbon components and reservoir temperature) resulted in the most accurate estimations of MMP (R2 = 0.99). To further enhance the explainability of the model, feature importance and shapely values analysis were conducted on the developed models, and the impact of each input feature on MMP was elaborated. Temperature, volatile/intermediate, and nonhydrocarbon components are the most influential parameters depending on the subset of parameters chosen. Moreover, the developed model using X_8 inputs performed significantly better (37 % more accurately) than three well-known empirical models from the literature.

Evaluation of immiscible CO2 injection in high water production reservoir in the Pannonian basin

Carbon dioxide (CO2) flooding is one of the most important and most used enhanced oil recovery (EOR) method because it does not only increase oil recovery efficiency but also is used as an underground CO2 storage. It is considered a very complex method as it involves knowing the fluid phase behavior with different CO2 concentrations. It should be noted that oil swelling (volume increase) with the dissolution of carbon dioxide has a significant effect on increase of oil recovery. When this occurs, a significant decrease in the viscosity of the oil is observed. In this study, a reservoir 3D simulation modeling approach was applied to evaluate immiscible and miscible CO2 flooding in a high WC reservoir. To reduce simulation time, the PVT composition was grouped into 5 fluid components. The 3-parameter, Peng-Robinson Equation of State (EOS) was used to match PVT experimental data by using the Schlumberger's ECLIPSE PVTi software. One-dimensional slim-tube model was defined using ECLIPSE 300 software to determine the minimum miscibility pressure (MMP) for injection of CO2. Beside this approach, an analytical MMP estimation was carried out using several correlations. Schlumberger Petrel software was used to set up a 3D simulation model of a static and dynamic model. Various scenarios of immiscible and CO2 injection have been simulated using ECLIPSE 300 software and these results have been compared.

A novel experimental-theoretical method to improve MMP estimation using VIT technique

In the gas injection process for enhanced oil recovery (EOR), the interfacial tension (IFT) and the minimum miscibility pressure (MMP) are two key parameters for evaluating the miscibility condition. The vanishing interfacial tension (VIT) method is an efficient method for measuring the interfacial tension of oil and gas versus pressure and can estimate the minimum miscibility pressure. A problem in the VIT test of a hydrocarbon system is that during this test, oil and gas densities vary due to the composition variation of both phases. But in the conventional interfacial measurement, this issue is ignored, and the initial and non-equilibrium densities are utilized for estimating the interfacial tension. In this study, it is tried to nominate a novel method for estimating the most accurate value of IFT. Hence, for this purpose, first a VIT test has been performed for a hydrocarbon gas and a live oil system and using the axisymmetric drop shape analysis (ADSA), the oil droplet has been analyzed, and the geometry factor, which has been defined in this paper, was calculated. After that, the volume of oil droplet and the gas have been measured and were used for vapor-liquid-equilibrium (VLE) or flash calculation using Peng-Robinson equation of state (PR-EOS). By VLE calculation fulfillment, the final composition of two phases is determined. Eventually, the densities of two phases have been calculated for VIT modification. Finally, the IFT results were plotted versus the pressure steps, and the MMP were estimated. Furthermore, to evaluate the results of VIT test and its modification, slim tube displacement (STD) test, which is known as the most reliable laboratory method for measuring the MMP in the oil industry, has been performed. The results show that the values obtained for MMP values for the VIT test with the corrected densities which are lower than the values obtained for the VIT test for not-corrected densities and the suggested experimental-theoretical method has been estimated the MMP more accurately. The MMP obtained by VIT test and modified VIT method was equal to 3858.72 and 3722.0 psi, respectively. Meanwhile, the slim tube test has measured the MMP equal to 3667.13 psi.

Reliable estimate of minimum miscibility pressure from multiple possible EOS models for a reservoir oil under data constraint

Multiple Equation of State (EOS) models are possible for a reservoir oil if limited laboratory data is used in the EOS model development; each EOS model has its own predicted minimum miscibility pressure (MMP) value with unknown reliability. This article presents a novel approach to estimate a reliable MMP value using the multiple MMP values calculated from multiple EOS models developed with limited laboratory data such as bubble point and density at bubble point. MMP predicted using the proposed approach has better accuracy than the estimated MMP from an EOS model developed using a large set of data at the regression stage. Each possible EOS model for a reservoir oil has a specific set of values of total Gibbs free energy of mixing (g) at bubble point and predicted MMP for a given injection gas. The MMP estimate is developed using the trend of g versus MMP (predicted) from multiple EOS models in the proposed approach. The AARD in MMP prediction for 22 oils is 5.21% using SRK EOS and 6.65% using PR EOS, which is lower than the earlier approach (7.6%) that uses all PVT, swelling test, and multi-contact test data in EOS model development.

Estimation of Minimum Miscibility Pressure for Hydrocarbon Gas Injection Based on EOS

The important parameter used for determining the probable application of miscible displacement is the MMP (minimum miscibility pressure). In enhanced oil recovery, the injection of hydrocarbon gases can be a highly efficient method to improve the productivity of the well especially if miscibility developed through the displacement process. There are a lot of experiments for measuring the value of the miscibility pressure, but they are expensive and take a lot of time, so it's better to use the mathematical equations because of it inexpensive and fast. This study focused on calculating MMP required to inject hydrocarbon gases into two reservoirs namely Sadi and Tanomaa/ East Baghdad field. Modified Peng Robenson Equation of State was used to estimate MMP values for the two samples. The parameters of this equation have been tuned by splitting the plus component and regression process to obtain the best match for PVT properties between the calculated and that measured in the laboratory. Then the MMPs value compared with the results most reliable correlation. Ternary diagram for these samples has been constructed to illustrate the occurrence of miscibility.

Improved Method for the Estimation of Minimum Miscibility Pressure for Pure and Impure CO2–Crude Oil Systems Using Gaussian Process Machine Learning Approach

AbstractThe minimum miscibility pressure (MMP) is one of the critical parameters needed in the successful design of a miscible gas injection for enhanced oil recovery purposes. In this study, we explore the capability of using the Gaussian process machine learning (GPML) approach, for accurate prediction of this vital property in both pure and impure CO2-injection streams. We first performed a sensitivity analysis of different kernels and then a comparative analysis with other techniques. The new GPML model, when compared with previously published predictive models, including both correlations and other machine learning (ML)/intelligent models, showed superior performance with the highest correlation coefficient and the lowest error metrics.

Estimation of minimum miscibility pressure during CO2 flooding in hydrocarbon reservoirs using an optimized neural network

CO2 flooding recovery strongly depends on the minimum miscibility pressure (MMP). Conventional tests to determine gas–oil MMP such as rising bubble apparatus and slim tube displacement are either costly or time consuming. In order to propose a quick and accurate model to determine MMP, a back-propagation neural network is presented for MMP prediction during pure and impure CO2 injections. Five new variables were screened as input parameters to the network. Next, the network was optimized using five evolutionary algorithms, and this work highlights that three of these evolutionary algorithms (e.g. Mind Evolutionary, Artificial Bee Colony, and Dragonfly) are firstly used to predict MMP. Then, data from the literature were input to the optimized network to train it. Statistical evaluation and graphical analyses were used to evaluate the performance of the proposed models and for comparison with published MMP correlates to obtain the optimal model for predicting MMP. The back-propagation model optimized using the dragonfly algorithm exhibited the highest accuracy among all those considered and MMP correlates; its coefficient of determination, average absolute percent relative error, root mean square error, and standard deviation were 0.965, 5.79%, 206.1, and 0.08, respectively. In addition, reservoir temperature was determined as the strongest MMP predictor (Pearson correlation = 0.63) based on sensitivity analysis.

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.

Four Methods to Estimate Minimum Miscibility Pressure of CO2‐Oil Based on Machine Learning

Summary of main observation and conclusionCO2 flooding accounts for a considerable proportion in gas flooding. Using CO2 as a gas displacement agent is benefit for enhanced oil recovery (EOR), and the alleviation of the greenhouse effect by the permanent storage of CO2 in the crust. Minimum miscibility pressure (MMP) of CO2‐oil is a key factor affecting EOR, which determines the yield and economic benefit of crude oil recovery. Therefore, it is of great importance to use fast, accurate and cheap prediction methods for MMP estimation. In the present study, to evaluate the reliability of four recently developed prediction models based on machine learning (i.e., neural network analysis (NNA), genetic function approximation (GFA), multiple linear regression (MLR), partial least squares (PLS)), 136 sets of data are selected for calculation via outlier analysis from 147 sets of data. Afterwards, we compared the four models with existing prediction models from the literature. The analysis of correlation coefficients and multiple error functions shows that the four models can solve the MMP prediction problem well, and the model using intelligent algorithm has a higher prediction accuracy than the simple linear model. Besides, intelligent methods based on similarity algorithm have little difference from each other. Finally, a sensitivity analysis was conducted.

An improved correlation to determine minimum miscibility pressure of CO2–oil system

An accurate and reliable estimation of minimum miscibility pressure (MMP) of CO2–oil system is a critical task for the design and implementation of CO2 miscible displacement process. In this study, an improved CO2–oil MMP correlation was developed to predict the MMP values for both pure and impure CO2 injection cases based on ten influential factors, i.e. reservoir temperature (TR), molecular weight of C7+ oil components (MWC7+), mole fraction of volatile oil components (xvol), mole fraction of C2C4 oil components (xC2-C4), mole fraction of C5C6 oil components (xC5-C6), and the gas stream mole fractions of CO2 (yCO2), H2S (yH2S), C1 (yC1), hydrocarbons (yHC) and N2 (yN2). The accuracy of the improved correlation was evaluated against experimental data reported in literature concurrently with those estimated by several renowned correlations. It was found that the improved correlation provided higher prediction accuracy and consistency with literature experimental data than other literature correlations. In addition, the predictive capability of the improved correlation was further validated by predicting an experimentally measured CO2–oil MMP data, and it showed an accurate result with the absolute deviation of 4.15%. Besides, the differential analysis of the improved correlation was analyzed to estimate the impact of parameters uncertainty in the original MMP data on the calculated results. Also, sensitivity analysis was performed to analyze the influence of each parameter on MMP qualitatively and quantitatively. The results revealed that the increase of xC2-C4, xC5-C6 and yH2S lead to the decrease of MMP, while the increase of TR, MWC7+, xvol, yCO2, yC1, yHC and yN2 tend to increase the MMP. Overall, the relevance of each parameter with MMP follows the order of TR > xC5-C6 > MWC7+ >xvol > yH2S > yHC > yCO2 >yC1 >yN2 >xC2-C4.

Estimation of minimum miscibility pressure of varied gas compositions and reservoir crude oil over a wide range of conditions using an artificial neural network model

Minimum miscibility pressure (MMP) is a key variable for monitoring miscibility between reservoir fluid and injection gas. Experimental and non-experimental methods are used to estimate MMP. Available miscibility correlations attempt to predict the minimum miscibility pressure for a specific type of gas. Here an artificial neural network (ANN) model is applied to a dataset involving 251 data records from around the world in a novel way to estimate the gas-crude oil MMP for a wide range of injected gases and crude oil compositions. This approach is relevant to sequestration projects in which injected gas compositions might vary significantly. The model is correlated with the reservoir temperature, concentrations of volatile (C1 and N2) and intermediate (C2, C3, C4, CO2 and H2S) fractions in the oil (Vol/Inter), C5+ molecular weight fractions in the oil and injected gas specific gravity. A key benefit of the ANN model is that MMP can be determined with reasonable accuracy for a wide range of oil and gas compositions. Statistical comparison of predictions shows that the developed ANN model yields better predictions than empirical-correlation methods. The ANN model predictions achieve a mean absolute percentage error of 13.46%, root mean square error of 3.6 and Pearson's correlation coefficient of 0.95. Sensitivity analysis reveals that injected gas specific gravity and temperature are the most important factors to consider when establishing appropriate miscible injection conditions. Among the available published correlations, the Yellig and Metcalfe correlation demonstrates good prediction performance, but it is not as accurate as the developed ANN model.

Estimation of minimum miscibility pressure (MMP) in enhanced oil recovery (EOR) process by N2 flooding using different computational schemes

Nitrogen is an effective agent to be considered in gas injection processes as a part of enhanced oil recovery (EOR) process. Successful design and implementation of nitrogen flooding scenarios greatly depend on accurate prediction of minimum miscibility pressure (MMP). This work highlights the application of three models namely GA-RBF, PSO-ANFIS and MLP for estimation of pure and impure MMP value in nitrogen-crude oil systems based on reliable and valid experimental data covering a wide range of operational parameters gathered from literature. Various statistical and graphical methods were utilized to evaluate the performance of the developed models. The results of the developed models are also compared with reported literature methods for estimation of nitrogen MMP. Results show that the proposed GA-RBF, CHPSO-ANFIS and MLP models are accurate for estimation of experimental data with overall average absolute relative errors of 1.9%, 2.92% and 3.59% and overall R2 values of 0.9847, 0.9686 and 0.9485, respectively. Moreover, the proposed models successfully outperform other literature methods and present more accurate and reliable results.

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.

Estimation of Minimum Miscibility Pressure for 〖CO〗_2 Flood Based on EOS

CO2 Gas is considered one of the unfavorable gases and it causes great air pollution. It’s possible to decrease this pollution by injecting gas in the oil reservoirs to provide a good miscibility and to increase the oil recovery factor. MMP was estimated by Peng Robinson equation of state (PR-EOS). South Rumila-63 (SULIAY) is involved for which the miscible displacement by is achievable based on the standard criteria for success EOR processes. A PVT report was available for the reservoir under study. It contains deferential liberation (DL) and constant composition expansion (CCE) tests. PVTi software is one of the (Eclipse V.2010) software’s packages, it has been used to achieve the goal. Many trials have been done to match the data of DL test by tuning some of the PR-EOS parameters through the regression analysis process, but no acceptable match was obtained especially for saturation pressure. However; splitting the mole fraction of (C6+) to many pseudo components was carried out, and then a regression analysis process was made again to improve the matching by tuning some of the PR-EOS parameters. A good estimate of saturation pressure and a good match of PVT properties was noted. Ternary diagram has been constructed to represent the phase behavior of -Oil and to calculate MMP for the South Rumila-63 (SULIAY) oil well.

Improved Minimum Miscibility Pressure Prediction for Gas Injection Process in Petroleum Reservoir

Determination of gas–oil minimum miscibility conditions is one of the important design parameters to improve the displacement efficiency of the hydrocarbon reservoir during enhanced oil recovery with gas injection. In this work, a support vector regression (SVR) model is developed using experimental data to estimate the minimum miscibility pressure (MMP) for various reservoir fluids and injection gases. Experimental MMP data taken from the reliable literature were used as input. Each data point input includes methane and intermediate components mole percent, plus fraction properties and reservoir temperature related to reservoir fluid and CO2, H2S, N2 and intermediate mole fractions, and intermediate properties of the injected gas. Experimental MMP is regarded as the model output. The database contains 135 datasets, from which 125 datasets were used for model development, and the rest were used for model evaluation. Genetic algorithm was implemented to optimize the SVR model parameters. The proposed data-driven model was verified by statistical validation data. The model results illustrate a correlation coefficient (R2) of 0.999. In addition, the SVR results demonstrate the proposed model to be a fast tool and a robust approach to map input space to output features. The SVR model was compared to popular data-driven MMP estimation models as well. This comparison presents an acceptable accuracy relative to this estimation model. Finally, the presented model was evaluated against a comprehensive theoretical model of slim tube compositional simulation on a trusted literature dataset.

A new estimating method of minimum miscibility pressure as a key parameter in designing CO2 gas injection process

Minimum miscibility pressure (MMP) is an important key parameter for designing any gas flooding project in enhancing oil recovery, whereas the injection must be operated at or above (MMP). This parameter will be determined before any field trial, because it is important for operator to plan suitable surface facilities and to develop injection conditions. MMP may be reliably estimated by traditional laboratory techniques, which are very costly and time consuming. Various empirical correlations for the estimation of MMP are available in literature however using them is not reliable as each correlation relates to a unique reservoir and fluid conditions. Hence none of these correlations can be used with enough confidence to estimate (MMP) and applying them is prone to errors. Therefore, the objective of this article is to search for a quick and sturdy mathematical correlation for the mathematical determination of (MMP) based on regression analysis within a shorter period of time. Finally, a comparison between the (MMP) calculated from the proposed correlation and the published ones was presented. Results ensure that the suggested new (MMP) correlation is the more accurate one.

A novel protocol for estimation of minimum miscibility pressure from slimtube experiments

A slimtube experiment is an industry-accepted method for estimating minimum miscible pressure (MMP) of a reservoir fluid with respect to a particular injection gas. The displacement efficiency of the gas is evaluated at immiscible as well as at multi-contact miscible pressure conditions. Although experimental protocols and operational aspects vary among investigators, the MMP is often inferred from the bend of the oil recovery curve versus pressure. From an equation of state (EOS) calibration point of view, the experiments conducted below and near the MMP are more valuable than the miscible runs. Since the number of slimtube runs is often limited to 4–6, this means that the mass transfer in the near-miscible may not be adequately captured and the uncertainty in the MMP estimation can be significant.In this work, a modification to the standard slimtube experimental protocol is presented to overcome the inconsistency in the interpretation of the MMP and to provide more information about the mass transfer in the near-miscible region close to the MMP. The new protocol takes advantage of the fact that chromatographic separation occurs during two-phase flow at pressures below the MMP and it is shown that the C1/C3 ratio in the produced gas is a very useful parameter to track as a function of pressure. If all slimtube runs are conducted at pressures below MMP, an efficient iterative procedure can be implemented to select a limited number of pressure steps leading towards the MMP.The proposed method requires compositional analysis of the produced gas, which adds to the cost of a study but may require fewer slimtube runs while yielding a more accurate estimation of the MMP.

Determination of minimum miscibility pressure in N2–crude oil system: A robust compositional model

Nitrogen has been valued as an economical alternative injection gas for gas-based enhanced oil recovery (EOR) processes. Minimum miscibility pressure (MMP) is the most important parameter to successfully design N2 flooding. In this communication, a data bank covering wide ranges of thermodynamic and compositional conditions was gathered from open literature. Afterward, a rigorous approach, namely least square support vector machine (LSSVM) optimized with coupled simulated annealing (CSA) was proposed to develop a reliable and robust model for the prediction of MMP of pure/impure N2–crude oil. The results of this study showed that the proposed model is more reliable and accurate than the pre-existing models in a wider range of thermodynamic and process conditions. The proposed model predicts the total dataset (84 MMP data points of pure N2, nitrogen mixture streams and lean gases) with an average absolute relative error of 5.17%. Finally, by employing the relevancy factor, it was found that the intermediate components of crude oil have the most significant impact on the nitrogen MMP estimation and Leverage approach shows that only two data points (2.4%) are outside the applicability domain of the model proving the reliability of the developed model.

Determination of minimum miscibility pressure in CO2-IOR projects with the aid of hybrid neuro-fuzzy system

Gas injection process is one of the most efficient improved oil recovery methods for conventional oil reservoirs. The efficiency of this process is strongly dependent on minimum miscibility pressure (MMP) which is usually determined through very expensive and time-consuming laboratory tests. So, this paper is concerned with the use of hybrid approach, combining particle swarm optimization and adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means clustering technique, aimed at an estimation of injected gas-reservoir oil MMP. The hybrid proposed model makes ANFIS is practical in dealing with complex and high dimensional MMP problem. The process of model building is done by considering the reservoir temperature, crude oil composition, composition of injected CO2 as input parameters and corresponding CO2-oil MMP information as target parameter. The validity of proposed model has been successfully approved by comparing with results obtained by well-known empirical correlations in published literature. The results show that the proposed model significantly outperformed those four correlations with the lowest mean absolute error of 0.0099, the lowest root mean square error of 0.0174, and the highest coefficient of determination of 0.9823. Therefore, the proposed hybrid model can be applied as an alternative method to yield more accurate results in CO2-oil MMP estimation.

A rigorous approach for determining interfacial tension and minimum miscibility pressure in paraffin-CO2 systems: Application to gas injection processes

Determination of interfacial tension (IFT) between the reservoir crude oil and the injecting gas as well as the minimum miscibility pressure (MMP) are the keys for successful gas injection process for enhanced oil recovery (EOR) in the matured oil fields. In this study, a novel supervised learning method called least square support vector machine (LSSVM) was developed to estimate IFT of paraffin-CO2 system. Besides, the MMP of the same system is estimated using the same model by using the vanishing interfacial tension (VIT) technique. The IFT was assumed to be an explicit function of pressure, temperature and molecular weight of paraffin, which was considered as the basis of the proposed model. The results showed that the proposed model is able to predict the IFT values with an average absolute percentage relative error of 4.7%. The highest relative error for estimation of MMP was found to be only 6.79%. Also, relevancy factor showed that pressure has the largest impact on the IFT of paraffin-CO2 systems. At the end, the Leverage approach demonstrated that the proposed model is statistically valid and acceptable and only 3.8% of the data points were out of the applicability domain of the model.