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Recently, intelligent models have become very popular among drilling engineers. These models have aided many researchers to deal with different problems in the area of drilling engineering. In this chapter, a review on the applications of intelligent modelling techniques in drilling engineering is presented. To this end, the published papers are categorized into six different topics, namely, Drilling fluids, Lost circulation problem, Stuck pipe, Flow patterns (FP) and frictional pressure loss (FPL) of two phase flow, Rate of penetration, and Other applications.

This chapter investigates the potential of nanoparticles (NPs) for enhanced oil recovery (EOR). Nanofluids are produced by the addition of NPs to fluids to intensify and ameliorate certain properties at low volume concentration. The EOR mechanisms of NPs are not yet fully recognised. Various mechanisms including: decreasing the interfacial tension, altering the wettability, and lowering the viscosity are related to the recovery of trapped oil in reservoir. Nanofluid flooding is an innovative chemical EOR approach whereby nanofluids are injected into oil reservoirs to impact oil displacement or enhance injectivity. The use of NPs in enhanced oil recovery can improve upstream productivity. The chapter lighlightes various types of NPs used in chemical EOR. The use of nanofluids can decrease the interfacial tension and viscosity, as well as altering the wettability of the rock to a more water-wet state.

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

Asphaltenes with complicated molecular structures consist of many aromatic portions, alkyl chains, and different functional groups. Various methods can determine their average structure. X-ray diffraction (XRD) is one of the most powerful techniques for investigating structural characteristics, especially in asphaltenes. The main goals of this paper are a comprehensive assessment of obtained crystallite parameters of asphaltene with various sources such as crude oil, vacuum residue, atmospheric residue, oil shale, coals, and asphalts, as well as investigation of the impact level of different kinds of processes such as hydro-treating, thermal cracking, thermal maturation, thermal decomposition, mild thermal processing reaction, aging, washing, catalytic aquathermolysis, and solvent addition. Results indicate that the coal-derived asphaltenes possess the highest aromaticity values and the extracted asphaltene of crude oil has the largest number of aromatic sheets in a cluster. Values belong to distances between aromatic sheets and saturated portions are almost equal in all categories of asphaltenes. Calculated XRD parameters are in good agreement with other techniques like Nuclear Magnetic Resonance (NMR), Raman spectroscopy, and Fourier transform infrared spectroscopy (FTIR). Thermal processes are generally accompanied by aromaticity increment, while aging causes aromaticity diminution. Notwithstanding some deficiencies about selected assumptions and approaches by the operator for data treating, XRD is considered a precise technique for determining structural parameters.

Thermal maturity level of crude oil samples is commonly investigated using popular biomarkers such as steranes and hopanes. Although crude oil's thermal maturity is an important goal for petroleum engineers and geochemists, fewer researches have been published about crude oil maturity than kerogen, source rock, and coal samples. This paper aims to assess the crude oil thermal maturity by asphaltene fraction analyzed by Raman spectroscopy and X-ray diffraction (XRD) methods for the first time. For this goal, the thermal maturity levels of three crude oil samples were investigated by geochemical parameters. In the next step, the asphaltene fractions of studied samples were precipitated and analyzed by two known Raman spectroscopy and X-ray diffraction methods to assess the applications of asphaltene fraction in determining the level of thermal maturity. Raman spectra indicated that during the maturation of organic matters, the amounts of disordered structures of asphaltenes diminish, and the position of the D1 band, as well as its area and intensity, are considered as maturity indicators. The Raman band separation (RBS) between two D1 and G bands, ratios of intensity and area of D1/G, and G's width are influenced by maturity. In this study, the RBS values increase from 210 cm−1 to 237 cm−1 by maturity increment. The results of XRD showed that the average distance between aromatic sheets (dm), the average height of the cluster perpendicular to the plain of sheets (Lc), aromaticity, and the ratio of intensities of two prominent detected bands of 002 and γ (I002/Iγ) are dependent on the maturity variations. Maturity increment resulted in increasing aromaticity (from 23% to 40%), I002/Iγ (from 0.24 to 0.34), and dm as well as decreasing Lc (from 33.84 °A to 29 °A) values. Since there is a good positive correlation between maturity and aromaticity and I002/Iγ, the cross plot of aromaticity versus I002/Iγ can be applied as a novel and proper tool for assessing the maturity of crude oil samples by the XRD patterns of their asphaltene fractions. Eventually, the results of this paper represent that RBS, intensity and area ratios of D1/G bands, G's width, aromaticity, Lc, and I002/Iγ parameters can be implemented for crude oil maturity assessment. The findings of this study can help for a better understanding of crude oil maturity level by the asphaltene fraction through a simple and inexpensive procedure.

Fluid viscosity is considered as one of the most important parameters for reservoir simulation, performance evaluation, designing production facilities, etc. In this communication, a robust model based on Genetic Programming (GP) approach was developed for prediction of under-saturated reservoir oil viscosity. A third order polynomial correlation for prediction of under-saturated oil viscosity as a function of bubble point viscosity, pressure differential (pressure minus bubble point pressure) and pressure ratio (pressure divided by bubble point pressure) was proposed. To this end, a large number of experimental viscosity databank including 601 data sets from various regions covering a wide range of reservoir conditions was collected from literature. Statistical and graphical error analyses were employed to evaluate the performance and accuracy of the model. The results indicate that the developed model is able to estimate oil viscosity with an average absolute percentage relative error of 4.47%. These results in addition to the graphical results confirmed the robustness and superiority of the developed model compared to the most well-known existing correlations of under-saturated oil viscosity. Additionally, the investigation of relative impact of input parameters on under-saturated reservoir oil viscosity demonstrates that bubble point viscosity has the greatest impact on oil viscosity.

The aim of present study is development of an accurate mathematical model for prediction of water activity in poly(ethylene glycol)-water solutions. The least square support vector approach (LSSVM) was implemented for this purpose. Four hundred thirteen experimental data points with different molecular weights of polymer ranging 150–20,000 were employed for model development. Average absolute relative deviation of the model was obtained 0.99% demonstrating excellent accuracy of the model. The results of the proposed model was compared with an existing predictive model available in literature illustrating that the proposed model is significantly superior and robustness in comparison with the other model. Besides, a sensitivity analysis was performed which shows that molecular weight of polymer has the most significant effect on activity of water. Furthermore, the Leverage approach was used to statistically check the performance of the model, which confirmed model validity and acceptability from a statistical point of view.

Among all physicochemical properties of ionic liquids (ILs), thermal conductivity has less been investigated both experimentally and theoretically. In this regard, experimental investigations and predictive models for thermal conductivity of ionic liquids have great importance for efficient design of heat transfer processes relevant to ILs, for instance in solar collectors. The aim of this study is to develop a robust precise model for prediction of thermal conductivity of ionic liquids. In order to estimate the thermal conductivity of pure ILs, a least square support vector machine was proposed based on 22 ionic liquids. The average absolute percent relative error for all studied systems is 1.03%, which is a satisfactory degree of accuracy for the proposed model. Also, the proposed model has higher accuracy compared to other models available in literature. In addition, the Leverage approach was implemented to check the reliability of the proposed model and the quality of experimental data. It was found that both model development and its predictions are statistically valid and correct and only few data points were located out of the applicability domain of the proposed model.

The phase equilibria of binary mixtures of CO2 + alcohols are crucial for the design and optimization of supercritical fluid applications with recently growing reliance on CO2 as the primary solvent at its supercritical conditions. Essentially, alcohols, as cosolvent, contribute to the solvating characteristic of supercritical CO2, which is de facto required for (polar) compound extraction in different industries. However, the proposed, less time-consuming alternatives to experimentally measuring the vapor-liquid equilibrium (VLE) data have been successful in such non-ideal mixtures only to a certain extent. In particular, the observed high deviation in existing models from the experimental values in correlating VLE data necessitates developing an intelligent, self-learning, unique model with more accuracy as well as applicability to a notably broader range of conditions. To this end, we have employed a novel Least-Squares Support Vector Machine (LSSVM) approach as a problem-independent, general-purpose tool. In this research, the LSSVM model is optimized with a Coupled Simulated Annealing (CSA) optimization algorithm. The hybrid model is based on a comprehensive databank of 531 reliable experimental VLE data that cover binary systems of CO2 + 13 types of short to medium chained alcohols at temperature and pressure overall ranges of 293.15–432.45 K and 5.2–213.91 bar, respectively. The statistical and graphical error analyses vividly demonstrate the supremacy and robustness of the developed CSA-LSSVM model, particularly compared to the conventional SRK and PR equations of state. However, minor deviations in mixtures containing Pentanol and Hexanol are observed, despite the overall model accuracy that strongly satisfies engineering purposes. We hypothesize that uncertainty in the corresponding experimental data could be the cause; we prove this by applying a Leverage approach, while studying the validity and applicability domain of the model. Furthermore, detailed sensitivity analysis is conducted through a relevancy factor, which suggests that the CO2 mole fraction is the most influential parameter on the studied binary mixture bubble/dew point pressures.

The main objective of this study was to develop soft computing approaches for prediction of physicochemical properties of IL mixtures including: density, heat capacity, thermal conductivity, and surface tension. The proposed models in this study are based on support vector machine (SVM), least square support vector machines (LSSVM), and group method of data handling type polynomial neural network (GMDH-PNN) systems. To find the LSSVM and SVM adjustable parameters, genetic algorithm (GA) as a meta-heuristic algorithm was utilized. The results showed that LSSVM is more robust and reliable for prediction of physicochemical properties of IL mixtures. The proposed GA-LSSVM model provides average absolute relative deviations of 0.38%, 0.18%, 0.77% and 1.18% for density, heat capacity, thermal conductivity, and surface tension, respectively, which demonstrates high accuracy of the model for prediction of physicochemical properties of IL mixtures.

Surfactant flooding has emerged as an interesting enhanced oil recovery (EOR) process due to wettability alteration and reduction of oil-water interfacial tension (IFT). Environmental concerns and high cost, cause synthetic surfactant flooding to become expensive and uneconomical in some circumstances. Natural surfactants are environment-friendly and less expensive than synthetic surfactants which recently have been proposed as alternatives for synthetic surfactants in EOR processes. However, the exact mechanism behind natural surfactant flooding as EOR process is an unsettled and complex issue that has not been completely understood. In this communication, the use of natural surfactants as an alternative for synthetic surfactants for wettability alteration of oil-wet rocks in EOR processes was investigated. Through the wide range of experiments, the performance of a natural surfactant named Cedar in the wettability alteration of carbonate and sandstone rocks was deeply studied. Moreover the effect of natural surfactant on oil-water interfacial tension was compared with common used synthetic surfactants. The results showed that Cedar is very efficient in wettability alteration of both carbonate and sandstone rocks and its effect is comparable with common used synthetic surfactants. In addition, it was found that the cationic surfactant is more effective than anionic surfactants in wettability alteration of carbonate reservoirs. On the other hand, the anionic surfactants are very effective in sandstone rocks. In addition, two distinct trends were observed for wettability alteration of surfactants at different salt concentrations. When the head of surfactant and rock surface carries the same charge, there is an optimum salinity for wettability alteration. Finally, the core flooding experiments demonstrated that natural surfactant can increase oil recovery efficiently.

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.

The unique and exciting properties of ionic liquids (ILs) have driven much interest in using them in many industrial applications. The thermophysical properties of pure ILs and their binary mixtures with water or alcohols are required for various usages. In this communication, in order to determine the density values of ILs, a large data bank containing a large number of ILs binary mixtures (1510 data sets) was gathered from previously published literature. The Least Square Support Vector Machine (LSSVM) strategy as a reliable and powerful computational prediction method was utilized for predicting the density of binary mixtures at atmospheric pressure. Additionally, a second LSSVM model was developed for predicting the density of 1-butyl-3-methylimidazolium tetrafluoroborate + methanol binary mixture at different pressures using 405 data points. Coupled Simulated Annealing (CSA) optimization tool was employed in order to optimize the parameters of both models. For the first model, the inputs were density of each compound at standard condition, temperature, and mole fraction of IL and for the second model temperature, pressure, and mole fraction of IL were considered as the input parameters. Moreover, in order to evaluate the performance of these models, various graphical and statistical error analyses were performed. These analyses demonstrated that the developed models are able to predict the density of ILs with high accuracy and reliability. Finally, in order to detect the applicability domain of the models, the Leverage approach was applied, which led to the detection of the probable data outliers. According to this approach, the entire experimental data sets in both models seem to be reliable except six data points in each model.

Due to unique physical and chemical properties of ionic liquids (ILs), they received lots of attention in many industrial fields and are widely under research. Ionic liquids, also are emerging as important components for applications in electrochemical devices. To develop their applications and achieving desire properties, they are usually mixed with organic solvents. Applying ionic liquids in many applications needs the accurate and reliable data of electrical conductivity of ILs and their mixtures. To this end, a total of 224 experimental data were collected from literature and divided randomly into two datasets: 179 data was selected as training set and the remained 45 data was used as a testing set. Afterwards, a reliable modeling technique is developed for modeling the electrical conductivity of the ILs ternary mixtures. This approach is called least square support vector machine (LSSVM). The model parameters were optimized using the method of couple simulated annealing (CSA). The input model parameters were, temperature of the system, melting point, molecular weight and mole percent of each component. A comprehensive error investigation was carried out, yielding the well accordance between the predictions of the model and experimental data. The presented model can predict the dependency of electrical conductivity variations with input variables. Moreover, the sensitivity analyses demonstrated that, among the selected input parameters, the average melting point of mixture has the largest effect on the electrical conductivity. Furthermore, suspected data were detected using the Leverage approach, residual, Williams plot and statistical hat matrix. Except seven data points, the all data appear to be reliable.

Asphaltene deposition leads to rigorous problems in petroleum industry such as relative permeability reduction, wettability alterations, and blockage of the flow with additional pressure drop in wellbore tubing, etc . Many attempts have been undertaken to develop reliable and accurate models to predict phase behavior of asphaltene. However, previously published models cannot couple static and dynamic behaviors of reservoir and its fluids. In this communication, a novel four-phase flow simulator has been developed considering asphaltene deposition as one of the phases, using finite volume formulation. A novel adaptive unstructured mesh generator technique has been introduced in which dynamic properties of reservoir are combined with static field parameters. The results of this study illustrated that the proposed methodology can accurately predict the oil sample properties, and the amount of asphaltene precipitation. Moreover, it was found that the proposed approach is faster and computationally less expensive compared to fine structured model. The proposed strategy can be applied in any reservoir simulator and provides asphaltene phase behavior and reservoir properties with high degree of accuracy.

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-CO 2 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-CO 2 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.

Asphaltene precipitation causes several problems during different stages of oil production in the reservoirs. Experimental measurement of asphaltene precipitation is cumbersome, expensive and tedious. In this communication, the amount of asphaltene precipitation during titration experiments was modeled as a function of easily measureable parameters including temperature, type of solvent, and solvent to oil dilution ratio. A large data bank of asphaltene precipitation was collected from different sources, covering a wide range of thermodynamic conditions and different types of crude oils. Least square support vector machine (LSSVM) optimized with a stochastic algorithm named couples simulated annealing (CSA) was employed for the purpose of modeling. The data bank was divided into four sections based on the type of solvent and solvent to oil dilution ratio. Subsequently, for each section a model was proposed and the results showed that all of the proposed models can predict the amount of asphaltene precipitation with enough accuracy. In general, the proposed CSA-LSSVM models can predict asphaltene precipitation with an average absolute relative error of 9.46%. The proposed models were compared to pre-existing models and both graphical and statistical analyses indicated the superiority of the proposed CSA-LSSVM models over the pre-existing ones. Finally, a mathematical model was used which not only defines the applicability domain of the proposed models, but also evaluates the quality of experimental data and detects the probable outliers. The results demonstrated that all of the proposed models are statistically valid and only 3.3% of the data may be recognized as the probable outliers.

This paper presents an artificial intelligence-based numerical investigation on the CO2 solubility in live and dead oils for possible CO2-enhanced oil recovery (EOR). A thorough smart modeling was accomplished by utilizing Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural network predictors integrated with seven vigorous optimization algorithms. Furthermore, Group Method of Data Handling (GMDH) approach was manipulated to achieve explicit mathematical expressions for the scope of the current study. The modeling was performed on a rich source of data derived from the previously published works. Assessments regarding all extended models demonstrated the Absolute Average Relative Error (AARD) ranges of 1.19%–3.47% and 1.63%–3.13% for live and dead oils, respectively. This indicates the prosperousness of all suggested models for anticipating the CO2 solubility in live/dead oil. A comparison between the proposed models indicated the marginally better performance of the MLP-LM (AARD = 1.19%) and MLP-SCG (AARD = 1.63%) in the case of live and dead oils, respectively. Additionally, the implemented models were compared against various published approaches, and the results revealed that the majority of our newly generated models outperform the prior approaches. In addition, the established GMDH-derived correlations were found to be the most truthful in comparison to other explicit literature correlations. These results provide significant insights for understanding the complex physicochemical processes of CO2-EOR and accurately predicting CO2 solubility in live and dead oils in reservoirs.

Wettability as related to an oil reservoir represents a fluid ‘s propensity to adsorb or stick to a solid surface in the presence of another immiscible liquid. Therefore, wettability is a key aspect in assessing the degree of recovery of oil from the reservoir. This paper aims at the investigation of the effect of resin, asphaltene, anionic surfactant, and hydrophilic nanoparticles on the wettability alteration of calcite and dolomite rocks. In order to provide a better insight into wettability alteration, several laboratory tests were carried out including energy dispersive X-ray (EDX) analysis, Fourier transform infrared spectroscopy (FTIR), and Scanning electron microscopy (SEM). The contact angles measured on the calcite and dolomite rock surfaces for resin (1000 ppm in toluene) after three weeks were 88° and 93°, respectively. The results of contact angle analyses as a screen for two rocks (calcite and dolomite) wettability showed that resin altered the wettability of rock samples to oil-wet. Also, asphaltene as a heavy and polar component has more influence than resin on the wettability alteration towards oil-wet state. The polarization interaction mechanism is the principle method for altering the wettability of samples by asphaltene and resin. EDX analysis illustrated that the amount of carbon and oxygen after aging rocks in oil, asphaltene, and resin increased, and the heteroatoms N and S were appeared on the calcite and dolomite surfaces. Afterwards, the combination of silica (SiO2) nanofluid and anionic surfactant (sodium dodecyl sulfate (SDS)) for modification of wettability of oil-wet carbonate surface was investigated. The most significant changes were observed after 24 h of contacts of rocks with a mixture of nanofluid and anionic surfactant. The contact angle decreased from about 88° to 16° for calcite and from 93° to 15° in the case of dolomite. The main mechanisms responsible for wettability modification by SiO2-SDS surfactant are: first) hydrogen bonding between the hydroxyl groups of silica nanoparticles and the tail of the anionic surfactant, second) the adsorption of SiO2-nanoparticles to the carbonate rock substrate due to a high degree of negative charges.

Remarkable thermal properties of ionic liquids (ILs) such as high heat capacity and thermal conductivity have raised expectations to enhance their thermophysical properties for heat transfer applications. Despite various studies measuring the thermal conductivity of ILs, reliable models to predict this property are yet to be proposed. In this study, accurate models to estimate the thermal conductivity of the variety of ILs have been developed. To this end, the thermal conductivity was predicted by three models: (I) a simple group contribution method based on temperature, pressure, molecular weight, boiling temperature, and acentric factor; (II) a model based on thermodynamic properties, pressure, and temperature of ILs; and (III) a model based on chemical structure, pressure, and temperature. To develop model (I) a simple correlation was used and for development of models, (II), and (III), intelligent approaches comprising radial basis function (RBF) and multilayer perceptron (MLP) neural networks were implemented. Different optimization techniques including genetic algorithm (GA), gravitational search algorithm (GSA), scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithm (LM) were utilized for optimization of the neural networks to accurately predict the thermal conductivity. To develop the models, 504 experimental data from 50 ILs over a wide range of pressure (100–20,000 KPa) and temperature (273–390 K) were used. The results reveal that although the empirical correlation does not predict the thermal conductivity of ILs accurately (mean absolute percent relative error (MAPRE) =12.26%), its intrinsic simplicity is yet valuable and is superior compared to the literature models. Model (II) (RBF-GSA with an MAPRE% of 1.051) shows better performance with more accurate predictions. Model (III) (RBF- GSA) also shows an acceptable accuracy (MAPRE% = 1.901). Finally, the results obtained by models developed in this study were compared with previous models/correlations/group contribution methods. The results revealed that the proposed models are more accurate with higher performance compared to other models in the literature.

Asphaltene deposition in the wellbore is considered as a major operational challenge while producing some crude oils. It has been claimed that, external fields, such as electric field and ultrasonic radiation, could help tackling this issue. Although the effect of electric field on the aggregation size and the aggregation rate of asphaltene particles has been investigated before, its correlation with asphaltene deposition in the presence and absence of ultrasonic radiation is not known. In this study, the asphaltene deposition on metal surfaces was first mimicked using an electrodeposition process, and then the effect of ultrasonic radiation on electrodeposition of asphaltene were measured and analysed. Our observations showed that the electric filed itself does not unstable the dissolved asphaltene, and the asphaltene deposition on electrodes, under the effect of DC electric filed, first requires the formation of asphaltene aggregates. The maximum deposition was generated at 2 kV/cm, the exposure time of 300 s to the electric field, and the weight ratio of toluene to heptane 4.2%. It was also found that the structure and composition of asphaltenes, especially their polarity and aromaticity, affect asphaltenes deposition. Among different adsorption kinetic models, the nth-order kinetics model was selected to estimate the amount of deposited asphaltene. Finally, simultaneous ultrasound radiation during the process of electrodeposition, reduced the deposition rate on the metal blades from an average of 65 wt % of the total asphaltene content to 10 wt%. The findings of this study highlight the role of composition and structural properties of asphaltene on its surface deposition in the wellbore. More importantly, simultaneous radiation of ultrasonic waves during electrodeposition process confirms the effectiveness of ultrasonic radiation, as a quite new asphatene remediation technique.

In this study, citric acid-coated magnetic (CACM) nanoparticles (NPs) were synthesized and utilized as an interfacial tension (IFT) reduction agent. In addition, the effect of three types of salts at different concentrations up to 150,000 ppm was investigated. These salts, NaCl, CaCl2, and MgCl2, showed the IFT values of 19.34, 16.24 and 12.23 mN/m, respectively, at the optimum salinity condition. The IFT decreased swiftly with increasing brine concentration at the beginning and then, increased after the optimum point due to the effect of asphaltenes and salts, respectively. Meanwhile, CACM NPs indicated a very significant capability to reduce the IFT value down to 3.99 mN/m without using any surfactant. The CACM NPs, among the other NPs used in this study, have shown a key role in IFT reduction due to their acidic feature (these NPs were coated by citric acid). Consequently, the combination of the NPs with surfactant at low salinity condition, showed major changes (for Fe2O3 and SiO2 NPs) and minor changes (for CACM NPs) at the interface as the movement of the surfactants by the NPs to the interface increases the surface-active agents at interface for reducing the IFT. The findings of this study can help to gain a better understanding of the IFT behavior using magnetite NPs in combination with surfactant at low salinity condition.

Accurate knowledge of thermal conductivity (TC) of nanofluids is emphasized in studies related to the thermophysical aspects of nanofluids. In this work, a comprehensive review of the most important theoretical, empirical, and computer-aided predictive models of TC of nanofluids is undertaken. Then, several intelligent models, including multilayer perceptron (MLP), radial basis function neural network (RBFNN) and least square support vector machine (LSSVM) were developed to predict relative TC of nanofluids using 3200 experimental points. The database encompasses 78 different nanofluids, covering extensive-ranged parameters; namely temperature ranging from −30.00 to 149.15 °C, particle volume fraction in the range of 0.01–11.22%, particle size from 5.00 to 150.00 nm, particle TC ranging from 1.20 to 1000.00 W/mK and base fluid TC of 0.11 to 0.69 W/mK. Combining the developed intelligent models into a committee machine intelligence system (CMIS) provided more accurate predictive model. The CMIS model exhibited very low AARE values of 0.843% during the training and 0.954% in the test phase. Moreover, a comparison of performances showed that CMIS largely outperforms the best theoretical and empirical models. Lastly, by performing Leverage approach, the statistical validity of CMIS was confirmed and the quality of the employed data was checked.

Colloidal behavior of asphaltene in normal alkane-toluene mixture has been under research over the past decades. However, the influence of asphaltene composition and its structure on asphaltene aggregation in such mixtures has not been fully understood yet. In this study, the colloidal behavior of asphaltene in normal alkane-toluene mixture was investigated using image processing approach. Three different types of asphaltenes were extracted from Iranian oil reservoirs. Asphaltenes were characterized by elemental analysis, X-Ray Diffraction (XRD), and Diffuse Reflectance Fourier Transform Infrared Spectroscopy (DRIFTS). The effects of asphaltene concentration, type of normal alkane, ratio of normal alkane to toluene, and structure of asphaltene on onset of asphaltene flocculation (OAF) and aggregates size were investigated. The onset of asphaltene flocculation decreases with increasing asphaltene concentration. A mathematical model was developed to predict OAF for different normal alkanes and asphaltenes. The proposed mathematical model has an exponential form with two constants, which should be tuned using experimental data. The results indicate that as asphaltene concentration increases, larger aggregates are formed with an average (AS) size between 8 and 15 μm and some aggregates greater than 100 μm may be formed. Moreover, the structure and composition of asphaltene is determinant of aggregates’ size. The lowest aggregates size refers to asphaltene C with the lowest number of fused rings, polarity, and aromaticity, which is the most stable asphaltene with the smallest aggregates size. Asphaltenes A and B have almost the same size as polarity and aromaticity both contribute to the aggregates size. The aggregates formed by n-C7 (AS≈ 7.5 to 8.5 μm) are larger than those of n-C10 (AS≈ 8.5 to 12 μm) and n-C16 (AS≈ 9 to 14 μm). In addition, increasing the ratio of normal alkane to toluene increases the size of asphaltene aggregates up to a maximum value and then gradually decreases.

Pollution from crude oil spills is a global concern owing to its destructive effect on the environment; in particular, the aquatic life. In this work, a facile synthesis method is applied to produce a novel highly hydrophobic zeolitic imidazolate framework (ZIF-8) coated onto carbon fiber (CF) fabric as a substrate without pre-modification. The product ZIF-8/CF was characterized by X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), and surface area estimation as well as sorption and desorption tests. The porous structure of ZIF-8/CF consisted of fixed layers of carbon fabric and nanocrystalline ZIF-8. The ZIF-8 crystals increased the water contact angle of the carbon fabric to 150°, whereas the oil contact angle was nearly 0°, creating a shield toward water penetration. ZIF-8/CF displayed high porosity, strong hydrophobicity, low density and high sorption capacity of the hydrocarbons; up to 24 times its weight. Recycling of the sorbed hydrocarbons and crude oil was achieved by squeezing the organic out of the ZIF-8-CF, followed by simple heating to 80 °C and two-stage washing of the sorbent using toluene and acetone, respectively. The ZIF-8/CF exhibited excellent reusability and recyclability (up to 20 cycles), without losing its efficiency, proving its high potency for cleaning up oil spills.

Nanoparticles (NPs) have been recently recognized as effective asphaltenes adsorbents and deposition deterrents. The objective of this work is to provide a critical review and highlight the limitations of literature findings on NP use for asphaltenes adsorption and subsequent oxidation. Literature reports showed that asphaltenes uptake by NPs increases with increasing asphaltenes aromaticity and polarity. Moreover, NPs exhibit a higher selectivity to asphaltenes in the presence of other oil constituents such as resins. Composite NPs are superior asphaltenes deposition inhibitors owing to a synergy arising from attaching inorganic NPs to a hydrocarbon. It is worth noting that most of the asphaltenes uptake values were collected from model solutions, and were calculated based on UV–Vis measurements, which have been recently shown not to be very reliable. Acidic NPs and small size NPs are considered better asphaltenes adsorbents, whereas basic NPs and large size NPs are reported as better asphaltenes oxidation promoters. In situ combustion, which is an important enhanced oil recovery method, can be improved in the presence of NPs. Two mechanisms have been proposed to explain the rapid oxidation of adsorbed asphaltenes; namely mass transfer enhancement and catalytic effect. There is a stronger evidence in support of enhanced asphaltenes exposure to the mass of flowing air. Lastly, the impact of different reservoir conditions on asphaltenes adsorption is presented. The findings of this review improve our understanding of asphaltenes adsorption and the oxidation of adsorbed asphaltenes as well as the challenges hindering the effective use of NPs in asphaltenes related problems at both laboratory and field scales.

The dead oil viscosity is a key parameter to numerous reservoir engineering problems such as modeling of (viscously-unstable) flow and transport in hydrocarbon reservoirs, sweep efficiency of enhanced oil recovery scenarios as well as the breakthrough times of the injected fluid. Prediction of this thermos-physical parameter, however, is of challenge due to nonlinear dependence on reservoir conditions as well as the crude oil characteristics. Previous studies have attempted to develop predictive empirical correlations or other intelligent models for dead oil viscosity; however, they often suffer from the lack of generality and required accuracy. In this work, based on a comprehensive databank from diverse geological sources, we develop three intelligent models –upon various schemes including simulated annealing programming, artificial neural network, and decision tree– for estimating dead oil viscosity. The latter may be used further for prediction of saturated and under-saturated oil viscosity as well. Our models function in wide range of temperatures and oil API gravity; hence, they can be employed as unified, general-in-purpose frameworks for universal prediction of dead oil viscosity. We compare the resulting novel frameworks with the pre-existing models available in the literature, and demonstrate the superiority of the decision tree-based model over others in terms of statistical (and graphical) error estimates as well as the (physical) validity of the model. The findings of this study can help for better understanding and more accurate management, simulation and prediction in different oil fields.

Relative permeability curves are one of the main factors that control the behavior of multi-phase flow in underground energy resources. The incorrect selection of relative permeability curves can lead to inaccurate predictions of the reservoir's future behavior. Accurate prediction of the reservoirs transient behavior is therefore vital as it influences subsequent political and management decisions. In this study, two different algorithms, namely GA (Genetic Algorithm) and Iter EnKF (Iterative Ensemble Kalman Filter), were used to estimate the optimal set of relative permeability curves aiding Corey's three-phase relative permeability correlation. A synthetic reservoir model was used as a benchmark to compare the accuracy and efficiency of the utilized algorithms. Sensitivity analysis was conducted over the type of cost function used within the optimization algorithm. Graphical results, such as time-evolved reservoir parameters, saturation maps, and mismatch of the relative permeability curves were used for comparison purposes. The comparison study unveiled that oil recovery is the best option to be included in the cost function with a mean AARD (Average Absolute Relative Deviation) of around 6%. Nonetheless, the inherent non-uniqueness of the problem showed that the best optimal case based on the mismatch analysis might not necessarily represent the true relative permeability curves. Overall, parameters such as “oil recovery” and “cumulative gas production” better captured the evolution of the well and reservoir state variables with time, probably due to the consideration of the history of production at each data point. Finally, a comparison between GA and Iter EnKF showed that the difference between the optimal solutions obtained using these two algorithms is almost insignificant. Computational time analysis showed that both algorithms have almost the same running time for small population sizes while Iter EnKF leads to lower computation times for population sizes larger than 50.

Water in oil emulsions have turned into interesting and attractive resources for enhanced oil recovery (EOR). The stability of emulsions is important for its efficiency as EOR agents. The objective of this study was to investigate the simultaneous influence of ultrasound waves and microorganisms on the stability of water-in-oil emulsions. To this end, using an Iranian crude oil, two types of waters, surfactants and three types of biosurfactants producing microorganisms the impacts of temperature, ultrasonic frequency, and radiation time on the stability, viscosity and surface tension of water-in-oil emulsions were studied. From the results of this study, it can be pointed out that compared to the conventional surface-active agents; biosurfactants have a higher impact on the stability of emulsions. In addition, under the optimum conditions of this study, the surface tension decreased about 15% and the stability increased nearby 20%. Ultrasonic radiation reduces emulsion stability by about 6%. The T.F. species have the greatest effect on the emulsion stability up to 70 °C (more than 85%). Micromodel flooding experiments showed that injection of T.F biosurfactants emulsion provides the highest oil recovery due to their high stability, high viscosity, and low surface tension.

In the last years, an appreciable effort has been directed toward developing empirical models to link the relative permeability of gas condensate reservoirs to the interfacial tension and velocity as well as saturation. However, these models suffer from non-universality and uncertainties in setting the tuning parameters. In order to alleviate the aforesaid infirmities in this study, comprehensive modeling was carried out by employing numerous smart computer-aided algorithms including Support Vector Regression (SVR), Least Square Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), and Gene Expression Programming (GEP) as predictors and Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Levenberg-Marquardt Algorithm (LMA), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG) and Randomized Polynomial Time (RP) as optimizers. To this end, a wide variety of reliable databanks encompasses more than 1000 data points from eights sets of experimental data was utilized in the training and testing steps of the modeling process. The predictors were integrated with optimization algorithms to assign the optimum tuning parameters of each model. The modeling was implemented in two different manners from the standpoint of models inputs: (1) 2-input (saturation and capillary number); (2) 3-input (saturation, interfacial tension, and capillary number). The results of the comparison between these strategies demonstrate more accuracy of the models when employing three independent parameters as the input (3-input). Among the developed models, the MLP-LMA modeling algorithm outperformed all other models with root mean square errors (RMSEs) of 0.035 and 0.019 for gas and condensate phases, respectively. At the end, in a comparison between both 2-input and 3-input MLP-LMA models and five traditional literature models, both smart modeling approaches were established themselves as the most accurate techniques for estimation of relative permeability in gas condensate reservoirs.

In this study, the least square support vector machine (LSSVM) as a robust approach along with genetic algorithm (GA) was utilized for prediction of SO2 solubility in ionic liquids (ILs). The proposed model used the pressure, temperature, critical temperature, boiling temperature, critical pressure, critical compressibility factor, and acentric factor as input parameters. To develop the highly accurate model, the 232 data points were randomly split into training and testing sets. This model provides an average absolute relative deviation (AARD) of 4.6%, illustrating good accuracy and validity. In addition, an artificial neural network (ANN) was developed for prediction of SO2 solubility in ionic liquids. A thorough comparison between the proposed LSSVM and ANN models was conducted through statistical and graphical tools demonstrating the superiority of the former over the latter to predict the solubility of SO2 in ILs. Afterward, a sensitivity analysis was performed on the LSSVM model, elucidating that the pressure and temperature have the greatest effects on the SO2 solubility. Furthermore, the Leverage approach identified that only 2.8% of data points are outside the applicability domain of the proposed LSSVM model, which highlights the model is statistically acceptable and can be used as a predictive approach.

Asphaltene precipitation causes rigorous problems in petroleum industry such as: relative permeability reduction, wettability alteration, blockage of the flow, etc. Therefore, accurate determination of onset pressures of asphaltene precipitation is necessary. These pressures can be obtained by experimental measurements on representative samples of the crude oils; however, laboratory analysis of crude oil samples is costly, time consuming and cumbersome. In this communication, three simple and accurate expressions have been proposed for prediction of lower and upper onset pressures of asphaltene precipitation as well as saturation pressures. To this end, 33 crude oil samples were collected from open literature sources. Afterward, two constrained multivariable search methods, namely generalized reduced gradient (GRG) and successive linear programming (SLP), were employed for modeling and expediting the process of achieving a good feasible solution. Then, comparative studies were conducted between the developed equations and equations of state as well as empirical correlations. The results illustrate that the developed equations are accurate, reliable and superior to all other published models. The results show that the proposed equations can predict lower onset pressure, upper onset pressure and saturation pressure with average absolute percent relative errors of 5.04%, 3.93%, and 3.81%, respectively. Besides, it is found that molecular weight of heptane-plus fraction has the greatest impact on the lower onset pressure, while methane has the most significant effect on both of the saturation and upper onset pressures.

The amount of dissolved gas in production oil has been always a great question in oil and gas industry. Solution gas oil ratio is considered as a representative for the fraction of gas which is dissolved in oil during different stages of oil production. Several experimental methods have been developed for measuring this parameter. However, experimental procedures are usually time consuming, tedious and expensive. Thus, development of analytical equations and empirical correlations for estimation of solution gas oil ratio is of vital importance. In this study, a novel learning approach called least square support vector machine (LSSVM) optimized by coupled simulated annealing (CSA) was developed for calculating solution gas oil ratio as a function of temperature, bubble point pressure, gas specific gravity and oil API. To this end, a large number of data points including more than a thousand data sets from multiple reservoirs covering a wide range of reservoir conditions and pressure-volume-temperature (PVT) properties were gathered from various sources of literature. In addition, several statistical and graphical analyses including Average Absolute Percentage Relative Error (AAPRE), Average Percentage Relative Error (APRE), Root Mean Square Error (RMSE) and Coefficient of Determination (R 2 ) were carried out to evaluate the accuracy and validity of model and to compare it with the most well-known implicit and explicit correlations of solution gas oil ratio estimation. Moreover, relevancy factor was employed to investigate the impact of each input parameter on solution gas oil ratio showing that bubble point pressure has the greatest effect on solution gas oil ratio. Finally, leverage approach was utilized to detect the data outliers and to find the applicability domain of the proposed model. The results in this study show that the developed model is able to estimate solution gas oil ratio with high accuracy and reliability making it possible to use the model in commercial software packages with various applications in oil and gas industry.

Crude oil viscosity is a key property needed for petroleum engineering analysis such as evaluation of fluid flow in porous media, reservoir performance, reservoir simulation, etc. This property is traditionally measured through expensive and time consuming laboratory measurements. In this communication, about 1500 dead oil viscosity data points of light and intermediate crude oil systems from various geological locations have been collected. Afterward, a soft computing approach, namely least square support vector machine (LSSVM), has been utilized to develop two distinct viscosity models for temperatures below and above 313.15 K. The parameters of these models have been optimized using coupled simulated annealing (CSA) optimization tool. The results of this study indicated that the developed models can predict dead oil viscosity at all temperatures and oil API gravities with enough accuracy. In addition, statistical and graphical error analyses illustrated that the proposed CSA-LSSCM models outperform all of pre-existing models. Besides, the relevancy factor showed that oil API gravity has the greatest effect on dead oil viscosity. Finally, the Leverage approach demonstrated that the proposed models are statistically valid and acceptable, and only 2% of the data points may be regarded as the probable outliers.

During the design of an enhanced oil recovery (EOR) method in a particular gas flooding project, measuring interfacial tension between injected gas and live reservoir oil samples is essential for estimating optimum miscible gas injection scenarios at reservoir conditions. In this study, during an experimental miscible gas injection project for one of the Iranian oil fields, natural liquefied gas (NGL) and Naphtha were selected to enrich injecting gas in order to study the gas composition impact on efficiency of miscibility process. Therefore, injecting gas was enriched by recombining with NGL and Naphtha samples with predefined ratios. Afterward, an axisymmetric drop shape analysis (ADSA) was utilized to measure interfacial tension between reservoir oil and five synthesized gas samples at depletion pressure steps and constant reservoir temperature. The results showed the optimum miscible gas enrichment scenario with minimum interfacial tension at depletion pressure steps. Additionally, recombining injecting gas sample with NGL led to reduce interfacial tension more effectively than recombined samples with Naphtha. The results of this study are helpful for successful design of gas injection scenarios in this filed, and reveal the role of gas composition in the IFT behavior of crude oil systems.

Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was utilized. Statistical and graphical error analyses have been employed separately to evaluate the accuracy and reliability of the proposed model. Furthermore, this model performance has been compared with a newly developed multilayer perceptron artificial neural network (MLP-ANN) model. The obtained results have shown the more robustness, efficiency and reliability of the proposed CSA-LSSVM model in comparison with the developed MLP-ANN model for the prediction of permeability in heterogeneous carbonate reservoirs. Estimations were found to be within acceptable agreement with the actual field data of permeability, with a root mean square error of approximately 0.42 for CSA-LSSVM model in testing phase, and a R-squared value of 0.98. Additionally, these error parameters for MLP-ANN are 0.68 and 0.89 in testing stage, respectively.

Precise evaluation of pure hydrocarbon and natural gas viscosity is vital for reliable reservoir characterization, simulation, transportation and optimum consumption. The most trustable sources of pure hydrocarbon and natural gas viscosity values are laboratory experiments. The need of new methods becomes important when there is not enough experimental data for specific composition, pressure, and temperature conditions. In this study, a promising approach is utilized for the prediction of viscosities of pure hydrocarbons as well as gas mixtures containing heavy hydrocarbon components and impurities such as carbon dioxide, nitrogen, helium, and hydrocarbon sulfide using over 3800 data sets. Gene Expression Programming (GEP) is employed to develop a general model for pure and natural gas viscosity. The proposed model is a function of pseudo reduced pressure, pseudo reduced temperature, molecular weight and density. In addition, comparative studies are performed between the results obtained by the GEP model and previously published empirical correlations. To this end, statistical and graphical error analyses are used simultaneously. The results obtained show a value of 4.9% for average absolute percent relative error which is a measure of relative absolute deviation from the experimental data. The results also propose that standard deviation as a sign of data scattering is only 0.0870. These observations illustrate that the GEP model is more robust, reliable and consistent than the existing correlations for prediction of pure and natural gas viscosity. Finally, the relevancy factor shows that molecular weight has the greatest effect on gas viscosity.

Nitrogen has been appeared as a competitive gas injection alternative for gas-based enhanced oil recovery (EOR) processes. Minimum miscibility pressure (MMP) is the most important parameter to successfully design N 2 flooding, which is traditionally measured through time consuming, expensive and cumbersome experiments. In this communication, genetic programming (GP) and constrained multivariable search methods have been combined to create a simple correlation for accurate determination of the MMP of N 2 -crude oil, based on the explicit functionality of reservoir temperature as well as thermodynamic properties of crude oil and injection gas. The parameters of the developed correlation include reservoir temperature, average critical temperature of injection gas, volatile and intermediate fractions of reservoir oil and heptane plus-fraction molecular weight of crude oil. A set of experimental data pool from the literature was collected to evaluate and compare the results of the developed correlation with pre-existing correlations through statistical and graphical error analyses. The results of this study illustrate that the proposed correlation is more reliable and accurate than the pre-existing models in a wide range of thermodynamic and process conditions. The proposed correlation predicts the total data set (93 MMP data of pure and N 2 mixture streams as well as lean gases) with an average absolute relative error of 10.02%. 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.

Carbon dioxide injection, which is widely used as an enhanced oil recovery (EOR) method, has the potential of being coupled with CO2 sequestration and reducing the emission of greenhouse gas. Hence, knowing the compressibility factor of carbon dioxide is of a vital significance. Compressibility factor (Z-factor) is traditionally measured through time consuming, expensive and cumbersome experiments. Hence, developing a fast, robust and accurate model for its estimation is necessary. In this study, a new reliable model on the basis of feed forward artificial neural networks is presented to predict CO2 compressibility factor. Reduced temperature and pressure were selected as the input parameters of the proposed model. To evaluate and compare the results of the developed model with pre-existing models, both statistical and graphical error analyses were employed. The results indicated that the proposed model is more reliable and accurate compared to pre-existing models in a wide range of temperature (up to 1,273.15 K) and pressure (up to 140 MPa). Furthermore, by employing the relevancy factor, the effect of pressure and temprature on the Z-factor of CO2 was compared for below and above the critical pressure of CO2, and the physcially expected trends were observed. Finally, to identify the probable outliers and applicability domain of the proposed ANN model, both numerical and graphical techniques based on Leverage approach were performed. The results illustrated that only 1.75% of the experimental data points were located out of the applicability domain of the proposed model. As a result, the developed model is reliable for the prediction of CO2 compressibility factor.

Accurate determination of pressure-volume-temperature (PVT) properties of petroleum reservoirs is essential in material balance calculations, inflow performance, well-test analysis, reservoir simulation, etc. Ideally, those data should be obtained experimentally; however, experimental measurements require accurate and enough sampling, and are time consuming, expensive and tedious. Therefore, seeking for a simple, reliable and accurate model for prediction of PVT properties of petroleum systems is of a vital importance. In this communication, a large PVT data bank, covering a wide range of thermodynamic conditions was collected from variety of geographical locations around the world. Afterward, gene expression programming (GEP) was employed to develop a universal model for solution gas: oil ratio. The proposed model is a function of bubble point pressure, gas specific gravity, and oil API gravity, and has a very simple format with only one tuning parameter. The proposed model was compared to both explicit and implicit models available in literature for prediction of solution gas: oil ratio, using statistical and graphical error analyses. The results of this study indicate that the proposed model is more accurate, reliable and efficient compared to all other published correlations.

Gas compressibility factor is necessary in most of chemical and petroleum engineering calculations. Accurate and fast calculation of this property is of a vital importance in a large number of simulators used in petroleum and gas engineering. In this study, a large data bank (978 data points), covering a wide range of natural gases, was collected from open literature sources. Afterwards, one of the newest and most powerful modeling approach, namely Wilcoxon generalized radial basis function network (WGRBFN) was employed to predict the compressibility factor of natural gases. The results obtained from the proposed model were compared to those of nine empirical correlations and five equations of state. Statistical and graphical error analyses demonstrated that the developed model can satisfactorily predict the compressibility factor of natural gases with an average absolute percent relative error of 2.3%. Moreover, it was demonstrated that the proposed model outperforms all of the studied empirical correlations and equations of state. Finally, to identify the probable outliers the Leverage approach was performed. All of the experimental data seem to be reliable except 2%. Therefore, the developed model is reliable for the prediction of natural gas compressibility factor in its applicability domain.

Among different types of enhanced oil recovery (EOR) methods, gas injection, or more specifically and commonly, CO2 injection, has been much in practice with the average recovery factor of 7%–23% over the globe. The large availability, low cost, and easily achieved miscibility condition are the main advantages of CO2 injection over the other sources of gases. In this chapter, a thorough investigation of fundamental and practical aspects of CO2 flooding into the hydrocarbon reservoirs is implemented. At the beginning, miscible and immiscible floods are introduced. Then the location and modes of injection as the essential points are reviewed. The experimental measures including core flood test, PVT experiments, and slime tube test are described. Afterward, the surface and subsurface CO2 injection facilities as well as the process design are discussed. In continuum, the applicability of the abovementioned recovery method is studied through both tight and gas reservoirs. Examining the CO2 impact on the surrounding environmental is analyzed in the final section of this chapter.