An Accurate Prediction of CO2 Minimum Miscibility Pressure (MMP) Using Alternating Conditional Expectation Algorithm (ACE)

Miscible gas injection processes are among the effective methods for enhanced oil recovery (EOR). A key parameter in the design of gas injection project is the minimum miscibility pressure (MMP), whereas local displacement efficiency from gas injection is highly dependent on the MMP. Because experimental determination of MMP is very expensive and timeconsuming, searching for fast and robust mathematical determination of gas-oil MMP is usually wished. A new CO2 MMP correlation based on Multiple-Linear-Regression modeling (MLR) technique has been successfully developed to more accurately estimate the CO2 MMP for a wide range of live and heavy crude oils. The newly developed CO2 MMP correlation is originated from CO2 MMP experimental data in addition to database from the worldwide published literature that covers 103 pure CO2 MMP data for various live and dead oil samples. The proposed model is trained by exploiting 80% (82 data points) of the data bank. This correlation is expressed as a function of reservoir temperature, C7+ molecular weight, mole fractions of; non-hydrocarbon components (CO2, H2S and N2), direct correlating components (C1, C2 and C5) and indirect correlating components (C3, C4, C6 and C7). Further, to investigate the authenticity in depth, the proposed correlation is compared with five most commonly used pure CO2 MMP correlations from the literature. A statistical comparison is performed for both training data set (82 data points) as well as testing data set (21 data points). It is found that the proposed CO2 MMP correlation provides the best reproduction of MMP data with a percentage average absolute error of 6.083% and 6.328% for training and testing categories respectively. Finally, the proposed model shows great performance using new set of samples compared with other correlations.

Over the years, the slim tube experiment has been the most commonly used laboratory technique for determination of minimum miscibility pressure (MMP) for designing field miscible floods. However, till now, the design of these experiments is not standardized, neither in terms of set-up nor procedure. Often, the set-up, characteristics of the slim tube coil and even the experimental procedures are left to the discretion of the experimentalist, leading to very uncertain and non-unique MMP values, to say the least. Not just the uniqueness is of great concern, but also the lack of measurement repeatability; two measurements using same fluid samples under the "same experimental conditions" may result in too different MMP values. Matching these experiments with any PVT simulation package may pose a challenge, which even if a match is achieved, may be of questionable reliability. It is on this premise that this work was based. Slim tube experiments were designed and performed to study the effects of some parameters on the uniqueness and repeatability of the MMP measurements. It was found that MMP measurements are different with different injection rates. The lowest tested rate showed slightly lower MMP and better recovery performance than the other tested rates. No clear trend was noted, however. MMP was found to be lower for the larger coil diameter of the two investigated. MMP decreased as the coil length increased. The decrease in MMP with increase in coil length followed a parabolic trend. It was, therefore, concluded that laboratory measurements of MMP using the slim tube apparatus is a function of not just the characteristics of the interacting fluids but also those of the coil used as well as the choice of injection rates. The experimental design and procedure need to be unified to produce more reliable MMP data. It is the recommendation of the authors to design the experimental injection rate based on the expected field gas injection, to use the largest coil diameter possible, and to design the coil length, based on the expected well spacing between the injector and producer.

One of the most effective enhanced oil recovery techniques is carbon dioxide (CO2) flooding. It is because of its high oil recovery rate and associated advantage on the environment. CO2 flooding has been broadly used and commercially proven. Depending on the reservoir and operational conditions, it can be implemented either in immiscible or miscible modes. Determining the injection mode depends on the minimum miscibility pressure (MMP). The conventional ways of determining the MMP (slim-tube experiments, supercritical extractor, etc.) are costly and time-consuming. A simulation approach has been deployed to determine the MMP of a synthetic reservoir model using a one-dimension compositional slim-tube model in ECLIPSE300. The MMP was also determined using the group method of data handling (GMDH) and other existing correlations. The MMP determined by slim-tube simulation was ~ 4176 psia. Comparing the MMPs of the correlations to the slim-tube simulation, the correlation by Maklavani et al. (Braz J Pet, 2010) was the most accurate with a mean absolute percentage error (MAPE) of 0.79%. The GDMH and the correlation of Firoozabadi and Khalid had MAPEs of 4.96% and 6.06%, respectively. The GMDH model introduced in this study was not as accurate as the correlation by Maklavani et al. (Braz J Pet, 2010) . The model could be improved, or other machine learning models with improved training and prediction could be deployed to estimate MMP more accurately. A synthetic reservoir model based on literature data was used to investigate the effect of the MMP and injection rate on oil recovery by CO2 flooding. The impact of MMP was investigated by the miscible and immiscible flooding scenarios. The injection rate was varied in each scenario to determine the optimum injection rate. However, the scenarios with optimum injection rates for both miscible and immiscible CO2 injection were compared to a waterflood scenario. The optimum injection rates for miscible and immiscible flooding were 2800 and 2200 Mscf/day, respectively. The oil recovery efficiencies for the optimum miscible and immiscible injection were 48% and 46%, respectively. In this study, miscible flooding proved to be the more effective method in oil recovery. However, considering the difference in oil recovery and the difficulty in achieving miscibility, the immiscible CO2 flooding proves to be the feasible option for improving oil recovery.

An improved correlation to estimate the minimum miscibility pressure of CO2 in crude oils for carbon capture, utilization, and storage projects

Summary Determination of the minimum miscible pressure (MMP) of the gas/oil system is essential for a successful carbon dioxide enhanced oil recovery (CO2-EOR) and carbon geological storage applications. Measurement of MMP can be performed with theoretical methods and experimental methods. The slim tube test (STT), the rising bubble apparatus (RBA), and the vanishing interfacial tension (VIT) technique can be considered as the effective experimental methods in the petroleum industry to measure the MMP of oil and gas systems. This paper presents a new experimental method for measuring MMP, named oil droplet volume dynamic analysis (ODVDA) method. The main principle of this method is to dynamically measure the change in the volume of the oil droplets suspended in CO2, and find the pressure point where the oil droplet volume reduction rate is equal to or just exceeds 15% in the interval of 600 seconds. This pressure point is considered to be MMP. The main advantage of ODVDA method over the previous methods is simple, time-saving, and more easily to conduct.

A Rapid Method for Determining CO2/oil MMP and Visual Observations of CO2/Oil Interactions at Reservoir Conditions

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

Summary Miscible gas injection is the most widely applied enhanced oil recovery (EOR) method in light oil carbonate reservoirs as a tertiary and secondary method. Miscible gas has high displacement efficiency and usually results in a low residual oil saturation (Sorm) in the parts of the reservoirs that are in contact with the gas. Accurate determination of Sorm and understanding the parameters that affect displacement efficiency are crucial for successful miscible gas EOR projects. In this paper, we present a comprehensive experimental program designed to investigate the effect of a number of parameters on oil recovery, displacement efficiency, and Sorm of miscible and near-miscible carbon dioxide (CO2) injection. The parameters investigated in this study are the experimental pressure, pore volume (PV) injected, injection rate, rock type, and initial water saturation (Swi). The coreflood experiments were performed using live crude oil at pressures starting below the minimum miscibility pressure (MMP) to pressure well above the MMP, using reservoir core samples of up to 1 ft long and 2 in. diameter. All CO2 injection experiments were performed using vertically oriented cores, with gas injection from the top to ensure stable displacement. The experimental results show that (1) Oil recovery decreases as pressure decreases with Sorm increasing by more than 20 saturation units as the pressure decreases from 4,250 psi to 2,700 psi; (2) CO2 breakthrough was much earlier at lower pressure, which leads to more CO2 recycling and potentially lower CO2 sequestration volume; (3) the recovery factor (RF) is strongly affected by the PV injected, and this effect is much more significant for the experiments performed at lower pressure; (4) the injection rate has an insignificant impact on oil recovery and Sorm for miscible or near-miscible CO2, due to the low interfacial tension (IFT) between oil and CO2; (5) rock heterogeneity has a strong effect on oil recovery and CO2 breakthrough and hence on CO2 recycling and economy of the projects; and (6) the presence of mobile water at the beginning of CO2 injection resulted in lower displacement efficiency and increased Sorm. However, this water blocking effect should be determined experimentally for a given reservoir rock/fluid system. The results of this study cannot be generalized for other reservoirs. The results of this study have important implications for the design and performance predictions of CO2 injection in the reservoirs under study. Starting CO2 injection at reservoir pressure, which, in some cases, is more than 1,500 psi above MMP, is recommended due to its superior displacement efficiency and less CO2 recycling due to later breakthrough. However, a higher pressure may negatively impact the required CO2 volume, the compression cost, and project economics.

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

An effective parameter in the miscible-CO2 enhanced oil recovery procedure is the minimum miscibility pressure (MMP) defined as the lowest pressure that the oil in place and the injected gas into reservoir achieve miscibility at a given temperature. Flue gases released from power plants can provide an available source of CO2, which would otherwise be emitted to the atmosphere, for injection into a reservoir. However, the costs related to gas extraction from flue gases is potentially high. Hence, greater understanding the role of impurities in miscibility characteristics between CO2 and reservoir fluids helps to establish which impurities are tolerable and which are not. In this study, we simulate the effects of the impurities nitrogen (N2), methane (C1), ethane (C2) and propane (C3) on CO2 MMP. The simulation results reveal that, as an impurity, nitrogen increases CO2–oil MMP more so than methane. On the other hand, increasing the propane (C3) content can lead to a significant decrease in CO2 MMP, whereas varying the concentrations of ethane (C2) does not have a significant effect on the minimum miscibility pressure of reservoir crude oil and CO2 gas. The novel relationships established are particularly valuable in circumstances where MMP experimental data are not available.

Minimum miscibility pressure (MMP) is defined as the minimum pressure at which the CO2 is dissolved in the oil phase inside the reservoir. Minimum miscibility pressure (MMP) plays a critical role in the CO2 injection process during miscible CO2 flooding. Experimentally, MMP is determined by slim-tube experiments, rising bubble method etc. However, experimental analysis is time consuming and can have high associated cost. Therefore, application of Artificial Intelligence (AI) techniques can assist in predicting the MMP based on the available input data. This will save significant time and efforts and predicted the MMP results faster and convenient way. Some authors have worked with AI tools to predict MMP, but the model proposed in this paper has a relatively lower error. Thus, the proposed model in this study is an improved model for the prediction of MMP for miscible CO2 flooding applications. A detailed optimization was carried out in this study for both ANN and ANFIS predictive tools. Single hidden layer with 12 neurons and ‘trainlm’ as training algorithm was found out after ANN optimization, whereas subtractive clustering with cluster radius of 0.3 was the optimum scenario for ANFIS technique. ANN prediction was overall better than ANFIS technique for the prediction of CO2 MMP.

Minimum miscibility pressure (MMP) is defined as the minimum pressure at which the CO2 is dissolved in the oil phase inside the reservoir. Minimum miscibility pressure (MMP) plays a critical role in the CO2 injection process during miscible CO2 flooding. Experimentally, MMP is determined by slim-tube experiments, rising bubble method etc. However, experimental analysis is time consuming and can have high associated cost. Therefore, application of Artificial Intelligence (AI) techniques can assist in predicting the MMP based on the available input data. This will save significant time and efforts and predicted the MMP results faster and convenient way. Some authors have worked with AI tools to predict MMP, but the model proposed in this paper has a relatively lower error. Thus, the proposed model in this study is an improved model for the prediction of MMP for miscible CO2 flooding applications. A detailed optimization was carried out in this study for both ANN and ANFIS predictive tools. Single hidden layer with 12 neurons and ‘trainlm’ as training algorithm was found out after ANN optimization, whereas subtractive clustering with cluster radius of 0.3 was the optimum scenario for ANFIS technique. ANN prediction was overall better than ANFIS technique for the prediction of CO2 MMP.

The determination of miscible characteristic is one of the key technologies for enhancing oil recovery of gas flooding. If the miscible characteristic at each development period of gas flooding can be known in real time, it will be helpful to guide gas flooding development scheme. The minimum miscible pressure (MMP) is mostly used to describe miscible characteristic. Currently, the MMP forecasting methods can be classified into two categories—the empirical method and theoretical calculation method. In this paper, the main controlling factors affecting MMP are analyzed combined with reservoir engineering method, phase equilibrium theory, reservoir numerical simulation technology, and so on. Based on this, new empirical and theoretical MMP forecasting model was built. Meanwhile, new ideas for improving forecasting accuracy through modifying miscible criterion were proposed. The calculation accuracies of the two MMP forecasting models can be improved to over 90% that is more accurate and adapted than other methods. This research result can supply new ideas for gas flooding MMP forecasting.

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.

CO2 injection has emerged as a pivotal technique in the petroleum industry, offering a cost-effective option to enhance oil recovery while contributing to improved environmental sustainability. The mass transfer of CO2 into reservoir fluids is the primary mechanism responsible for CO2 solubility trapping and enhancement of oil recovery. The minimum miscibility pressure between CO2 and crude oils controls the performance of any CO2-enhanced oil recovery (EOR) and storage project. This paper investigates the influence of reservoir conditions on the CO2-oil miscibility during CO2 injection in oil reservoirs. To do so, pendant drop tests were conducted, and CO2-oil interfacial tension (IFT) was measured at different pressures and two temperatures. The minimum miscibility pressure (MMP) and first contact miscibility pressure (FCM) between the CO2 and crude oil were estimated from the measured IFT values by applying the vanishing interfacial tension (VIT) technique. The measured MMP was found to increase with reservoir temperature as MMP increased from 1175 psi at T=27°C to 2007 psi at T=70°C. Similarly, the FCM pressure was found to significantly increase with reservoir temperature. This research provides a valuable guide for screening oil reservoirs suitable for EOR and CO2 storage.