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Abstract Hydrogen is recognized as a clean energy replacement for non-renewable fossil fuels, and the utilization of metal-organic frameworks (MOFs) for hydrogen storage has gained considerable interest in recent years. In this study, hydrogen storage in MOFs was estimated using white-box methods, namely group method of data handling (GMDH), genetic programming (GP), and gene expression programming (GEP), which are robust soft-computing methods known for generating innovative correlations. To this end, temperature, pressure, pore volume, and surface area were implemented as input parameters for constructing these robust correlations. After that, the superiority of the established correlations was demonstrated through multiple statistical and graphical error assessment. The results indicated, the GMDH model demonstrates the highest accuracy with root mean square error (RMSE), and mean absolute error (MAE) values of 0.410 and 0.307, respectively. However, the GEP model’s accuracy was comparable to that of the GMDH model. In addition, sensitivity assessment showed that the pore volume and the pressure exhibit the strongest linear and non-linear relationships, respectively, with the H2 storage in MOFs. This was demonstrated by a Pearson correlation coefficient of 0.5 and a Spearman correlation coefficient of 0.56, respectively. Furthermore, temperature had a minimal negative impact on the H2 storage in MOFs according to Pearson, Spearman, and Kendall coefficients. Finally, to confirm the findings of the GMDH model, the leverage approach was applied, demonstrating that 96% of the data falls within the acceptable region, confirming the statistical reliability of the developed models.

Abstract Geothermal energy, oil industry, and underground gas storage technology require deep drilling. Although oil-based drilling fluids have been widely used, they cause environmental issues. Environmentally friendly Aphronic fluid has emerged as an alternative to oil-based drilling fluid. Despite various investigations on Aphron’s performance, the effect of temperature has not yet been comprehensively studied. In this study, Aphronic fluid was developed with xanthan gum (XG) as a polymer and alpha-olefin sulfonate (AOS) as a surfactant. Furthermore, another Aphronic fluid was developed with xanthan gum (XG) as a polymer and sodium dodecyl sulfate (SDS) as a surfactant. This study evaluates their rheological properties, including shear rate (ranging from 0.1 to 10 s− 1), across a range of temperatures (25 ℃, 49 ℃, 71 ℃, and 93 ℃). Stability testing was conducted at ambient temperature, and rheology tests were performed at various temperatures. It was found that the rheological properties decrease as the temperature increases. An increase in the temperature and shear rate decreases the viscosity of the drilling fluid. AOS demonstrated higher stability and less reduction in rheological properties at elevated temperatures than SDS. This difference can be attributed to SDS’s tendency to denature the XG polymer, leading to a notable decrease in viscosity. Maintaining viscosity is crucial for fluid stability and enhancement of rheological properties. Moreover, the best stability was achieved at 1.5 times the critical micelle concentration (CMC). This behavior is consistent with the Gibbs elasticity theory, indicating that the optimal concentration for both AOS and SDS surfactants is 1.5 times the CMC. Deviation from this optimal concentration resulted in decreased system stability. The determination of the CMC value showed a broader region rather than a singular point, potentially confirming the Gibbs-Marangoni theory. The implications of this research underscore the importance of selecting the appropriate Aphronic fluid formula for drilling operations to ensure stability and rheological properties conducive to overcoming reservoir pressure and accurately facilitating the cutting transfer process. This research highlights the need to choose the right Aphronic fluid formula for drilling. Using the right formula helps in drilling to ensure stability and rheological properties conducive to overcoming reservoir pressure and accurately facilitating the cutting transfer process.

Abstract Due to the unique properties of nanoparticles (NPs), their application has been proposed as an innovative and promising enhanced oil recovery (EOR) technique. They enhance oil recovery by improving EOR mechanisms including decreasing interfacial tension (IFT), wettability alteration to water-wet, and preventing asphaltene precipitation. In this study, Fe3O4@Gelatin NPs were synthesized by a convenient and single-step method and then investigated for EOR purposes for the first time. These NPS were characterized by Transmission Electron Microscopy (TEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). Afterward, various experiments including contact angle, interfacial tension (IFT), and micromodel flooding were performed to evaluate the capability of Fe3O4@Gelatin NPs for EOR application. Based on the results of IFT measurements, the synthesized NPs caused the highest reduction in IFT of crude oil-seawater compared to Fe3O4 NPs and gelatin. This significant reduction is due to the synergistic effects of Fe3O4 NPs and gelatin in the form of a nanocomposite. According to the results from contact angle measurements, the Fe3O4@Gelatin NPs could significantly change the glass surface wettability from oil-wet to water-wet, which showed a better performance than Fe3O4 NPs. The surface modification of Fe3O4 NPs with gelatin causes to enhance the capability of these NPs for wettability alteration because the gelatin has surface active property. In the micromodel flooding tests, the seawater containing 0.06 wt. % Fe3O4@Gelatin NPs had the highest oil recovery factor (RF = 42.9%) compared to the seawater containing 0.06 wt. % Fe3O4 NPs (RF = 33.92%) and only seawater (RF = 26.57%), due to more IFT reduction and altering the surface wettability from oil-wet to strongly water-wet. Therefore, according to the results of this research, the synthesized NPs can be a good option for EOR.

Abstract Because a significant portion of oil remains in carbonate reservoirs, efficient techniques are essential to increase oil recovery from carbonate reservoirs. Wettability alteration is crucial for enhanced oil recovery (EOR) from oil-wet reservoirs. This study investigates the impact of different substances on the wettability of dolomite and calcite rocks. The substances include silicon dioxide (SiO2) and iron oxide (Fe3O4) nanofluids, gelatin biopolymer, surfactants (sodium dodecyl sulfate (SDS)), Fe3O4/SDS, seawater, and salt solutions (sodium chloride (NaCl) and calcium chloride (CaCl2)). Initially, water-wet rocks were exposed to crude oil for 22 days, resulting in significant contact angle changes. Dolomite and calcite contact angles increased from 56.50° and 50.70° to 107.70° and 104.00°, respectively, due to the presence of heavy and polar elements in the oil. The impact of aging time (7 and 11 days) on rock wettability was studied. Oil-wet rocks were treated with SiO2 and Fe3O4 nanofluids and SDS surfactants for 11 days. The contact angles of the treated rocks decreased significantly. For instance, the contact angles of dolomite and calcite treated with SDS surfactants decreased to 39.07° and 27.38°, respectively, indicating water-wet conditions. Dolomite and calcite surfaces aged with gelatin decreased the contact angles to 38.40° and 34.52°, respectively. Treatment with SiO2 nanofluid reduced the contact angles of dolomite and calcite to 54.27° and 53.17°, respectively, while treatment with Fe3O4 nanofluid decreased the contact angles to 46.08° and 51.16°, respectively. Using Fe3O4/gelatin nanocomposite resulted in contact angles of 26.00° for dolomite and 24.10° for calcite. The wettability alteration mechanism in nanofluids is attributed to structural disjoining pressure. Additionally, NaCl and CaCl2 solutions induced water-wet conditions, known as the salting-out effect, on dolomite and calcite specimens. Consequently, this study demonstrates the potential of various substances, such as nanofluids, surfactants, and salt solutions, to modify rock wettability and improve conditions for enhanced oil recovery.

Abstract Nanofluids have the capacity to reduce interfacial tension (IFT) of crude oil and water for enhanced oil recovery (EOR) operations, but traditional nanoparticles are limited in tight reservoirs due to their inappropriate size for micro-nano pores and their tendency to aggregate. In this paper, Graphene Quantum Dots (GQDs) with simple and favorable properties are developed, and their performance and mechanism for reducing IFT are evaluated. The paper also aims to explore the effects of GQD precursor type, synthesis duration, and molar percentages of precursors on reducing IFT. For this purpose, citric acid was used as a carbon source, and ethylenediamine, urea, and thiourea were used as nitrogen sources to synthesize different GQDs. FTIR, XPS, HR-TEM, XRD, UV visible, and PL photoluminescence were used to identify the GQDs’ characteristics. The highest IFT reduction value is achieved by using 1000 ppm ethylenediamine-derived GQDs, which reduces the IFT from 19.03 to 0.70 mN/m at 200,000 ppm NaCl concentrations without using any surfactants. The XPS analysis revealed that ethylenediamine-derived GQDs exhibit higher pyrrolic nitrogen content and a relative intensity ratio of sp3 C/sp2 C. It has been identified that the presence of more polar atomic bonds on the surfaces of GQDs decreases the ability of GQDs to reduce IFT. In this way, urea-derived and thiourea-derived GQDs exhibit less capability for IFT reductions from 19.03 to 12.33 and 18.04 mN/m at 1000 ppm GQDs and 200,000 ppm NaCl concentration, respectively. Furthermore, ethylenediamine-derived and urea-derived GQDs perform optimally at approximately 5 and 10 h, respectively, with a precursor molar ratio of 3 (mole ethylenediamine/urea per mole citric acid), while thiourea-derived GQDs show no significant IFT change with varying synthesis times or precursor molar ratio. The developed GQDs provide a promising solution for EOR operations in unconventional reservoirs by significantly decreasing the IFT between crude oil and nanofluids.

Abstract The urgency to mitigate carbon dioxide (CO2) emissions and combat climate change has spurred the development of effective CO2 capture technologies. One of the industry’s most well-known CO2 collection processes is CO2 absorption utilizing amine solvents. However, designing a successful amine scrubbing system in power plants requires precise prediction of CO2 absorption in aqueous amine solutions under various operating circumstances. Using aqueous piperazine (PZ) solutions for chemical absorption is promising due to the favorable reactivity of PZ with CO2. In this study, a comprehensive evaluation of PZ solution performance in CO2 capturing, employing the white-box algorithms, namely, Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH), was performed. Through extensive experimentation and data analysis, several correlations were developed with high and acceptable R2 values, such as 0.933 for GP, 0.949 for GEP, and 0.889 for GMDH, which shows high accuracy and reliability in predicting the CO2 capture efficiency of PZ solutions under varying operating conditions. The results of sensitivity analysis revealed that CO2 partial pressure increased CO2 absorption, while PZ concentration and temperature had negative and decreasing effects. These insights provide essential guidance for optimizing process conditions to enhance the CO2 capture efficiency. Finally, the leverage method was used to assess the reliability of both experimental and predicted data from white-box algorithms. This analysis identified potential outliers and validated the accuracy of the model’s predictions, enhancing the credibility of developed correlations and demonstrating the robustness of the approach.

Abstract Honing the quality of cement plays a pivotal role in the petroleum industry throughout drilling operations, undoubtedly. In cement plants, producing high-quality clinker is essential for improving oil well maintenance, reducing expenditures, enhancing safety, and more, demonstrating its importance in the long term. Despite tight control of clinker operational conditions, cement kiln operators can still produce dusty clinker. This work uniquely establishes the previously unexplored relationship between dusty clinker and the qualitative characteristics of class G oil well cement slurries, offering a practical solution to prevent the production of poor-quality cement in factories. All API tests were done according to API Specification 10 A. Furthermore, a thorough microscopic analysis was conducted on both standard and dusty clinkers to establish a connection between the properties of class G oil well cement slurries and the changes in crystal size observed in the minerals of dusty clinker. According to the studies conducted in this regard, changing the size of the cement particles has a considerable influence on the cement performance. Here, results revealed that increasing the alite (C3S) crystal size to 60–70 microns in dusty clinker led to a roughly 20-minute increase in the average thickening time. In addition, the compressive strength cured at 38 and 60 °C for 8 h, decreased by 1.47 and 2.89 MPa, respectively. More importantly, it was found that the average maximum consistency in dusty clinker oil well cement increased by almost 6.4 Bc over a 15 to 30-minute period.

In the oil industry, gas lift is essential for facilitating fluid flow toward the production unit. However, the challenge lies in balancing gas availability constraints to achieve maximum efficiency in an oil field. This study utilizes the integrated production modeling (IPM) software to simulate an oil field operation in Iran. To this end, 154 data points constructed by a central composite design (CCD) experiment were utilized to develop neural network models. Therefore, four robust models, including multilayer perceptron (MLP), radial basis function (RBF), general regression neural network (GRNN), and cascade forward neural network (CFNN), were implemented for modeling. In addition, the net present value (NPV) serves as the objective function. To optimize the selected input variables, including tubing inside diameter, gas injection rate, and separator pressure, various optimization algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), genetic algorithm (GA), and a Novel optimization algorithm in a gas-lift study called grey wolf optimization (GWO), were utilized considering the constraint of the limited available gas. A penalty function was used to incorporate this constraint into the optimization procedure. There has previously been much research in the area of gas lift optimization. However, robust neural networks (GRNN and CFNN) have not been used for integrated production system modeling, nor have GWO algorithms been used to maximize the production or NPV in gas lift operations until now. The results for model errors were found to be %2.09, %2.99, %10.68, and %1.75 for MLP, RBF, GRNN, and CFNN, respectively. These findings imply that the CFNN model is more efficient. Also, comparing the GWO approach to other algorithms, the largest NPV ($788,512,038$) was yielded with less sensitivity of its adjustable parameters. Thereupon, NPV and cumulated oil production indicate a significant increase compared to ordinary NPV and oil production with values of 351,087,876.4 $ and 14,308 STB, respectively. High NPV effectively captures the overall added value of the project and, as a benchmark, helps to make informed decisions about investment and resource allocation, ultimately driving economic growth and increasing competitiveness in using this method.

Abstract Carbon dioxide (CO2) is the main greenhouse gas that drives global warming, climate change, and other environmental issues. CO2 absorption using amine solvents stands out as one of the most well-known industrial technologies of CO2 capture. However, accurate prediction of CO2 absorption in aqueous amine solutions under different operating conditions is crucial for designing an efficient amine scrubbing system in power plants. In this work, CO2 solubility in aqueous piperazine (PZ) solutions was modeled using 517 experimental data points covering a temperature range of 298 to 373 K, PZ concentration of 0.1 to 6.2 mol/L (M), and CO2 partial pressure of 0.03 to 7399 kPa. To this end, four robust machine learning algorithms, including gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), and adaptive boosting decision trees (AdaBoost-DT) were utilized. Among the developed models, the CatBoost model presented the highest accuracy with an overall determination coefficient (R2) of 0.9953 and an average absolute relative error of 2.36%. Sensitivity analysis revealed that CO2 partial pressure had the greatest influence on CO2 absorption in aqueous PZ solutions, followed by PZ concentration and temperature. Moreover, CO2 partial pressure positively influenced CO2 absorption in aqueous PZ solutions, while PZ concentration and temperature exhibited negative effects. Finally, the leverage technique indicated that both the experimental data bank used for modeling and the model’s estimates were statistically acceptable and valid showing only 8 points (∼1.5% of total data) as possible suspected data.

Abstract Over time, with the increase in population and the subsequent increase in energy consumption and also due to the non-renewability of fossil fuels, the study of alternative fuels has increased. One of these fuels is biodiesel, which is a suitable alternative to fossil fuels such as diesel and received much attention from researchers today. For this reason, measuring the physical properties of biodiesel is of great importance. Due to the high cost and time-consuming nature of laboratory methods, numerical methods are used to estimate material properties. The novelty of this research was the use of two white box models, including Group method of data handling (GMDH) and Gene expression programming (GEP), which work on the basis of artificial intelligence. By using these models, two simple mathematical equations with high accuracy were presented to predict the surface tension of biodiesel. These models can be used at different temperatures and molecular weights. To do modeling, 78 laboratory data available in the literature were gathered and the data were randomly divided into two groups, train and test, in a ratio of 80 and 20. The input parameters include mass fraction of fatty acid ethyl esters and temperature (T), and esters are divided into three groups according to their molecular weight: less than 200 (Mw1), between 200 and 300 (Mw2), and greater than 300 (Mw3). The statistical error parameters were calculated for the two models developed in this research and after comparing the results, it was found that the GMDH model estimates the surface tension of biodiesel with a higher accuracy. The average absolute relative error for GMDH and GEP models was reported as 0.97 and 1.89, respectively. Also, other statistical error parameters of GMDH such as RMSE, SD, and R2 for the GMDH model were obtained as 0.444, 0.000233, and 0.9233, respectively. Moreover, sensitivity analysis showed that temperature has the highest impact on the surface tension of biodiesel, which is also an inverse effect. Finally, suspicious laboratory and outlier data points were identified using the Leverage technique. According to this analysis, only five data points were identified as outliers and suspicious laboratory data.

Abstract The growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase equilibria of this gas with water. Therefore, accurate prediction of CO2 solubility in water becomes an important thermodynamic property. This study focused on developing two powerful intelligent models, namely gradient boosting (GBoost) and light gradient boosting machine (LightGBM) that predict CO2 solubility in water with high accuracy. The results revealed the outperformance of the GBoost model with root mean square error (RMSE) and determination coefficient (R2) of 0.137 mol/kg and 0.9976, respectively. The trend analysis demonstrated that the developed models were highly capable of detecting the physical trend of CO2 solubility in water across various pressure and temperature ranges. Moreover, the Leverage technique was employed to identify suspected data points as well as the applicability domain of the proposed models. The results showed that less than 5% of the data points were detected as outliers representing the large applicability domain of intelligent models. The outcome of this research provided insight into the potential of intelligent models in predicting solubility of CO2 in pure water.

Abstract Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06–298.9 MPa) and temperatures (253.15–573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Abstract Precise measurement and prediction of the fluid flow rates in production wells are crucial for anticipating the production volume and hydrocarbon recovery and creating a steady and controllable flow regime in such wells. This study suggests two approaches to predict the flow rate through wellhead chokes. The first is a data-driven approach using different methods, namely: Adaptive boosting support vector regression (Adaboost-SVR), multivariate adaptive regression spline (MARS), radial basis function (RBF), and multilayer perceptron (MLP) with three algorithms: Levenberg–Marquardt (LM), bayesian-regularization (BR), and scaled conjugate gradient (SCG). The second is a developed correlation that depends on wellhead pressure (Pwh), gas-to-liquid ratio (GLR), and choke size (Dc). A dataset of 565 data points is available for model development. The performance of the two suggested approaches is compared with earlier correlations. Results revealed that the proposed models outperform the existing ones, with the Adaboost-SVR model showing the best performance with an average absolute percent relative error (AAPRE) of 5.15% and a correlation coefficient of 0.9784. Additionally, the results indicated that the developed correlation resulted in better predictions compared to the earlier ones. Furthermore, a sensitivity analysis of the input variable was also investigated in this study and revealed that the choke size variable had the most significant effect, while the Pwh and GLR showed a slight effect on the liquid rate. Eventually, the leverage approach showed that only 2.1% of the data points were in the suspicious range.

Abstract Accurate prediction of fuel deposition during crude oil pyrolysis is pivotal for sustaining the combustion front and ensuring the effectiveness of in-situ combustion enhanced oil recovery (ISC EOR). Employing 2071 experimental TGA datasets from 13 diverse crude oil samples extracted from the literature, this study sought to precisely model crude oil pyrolysis. A suite of robust machine learning techniques, encompassing three black-box approaches (Categorical Gradient Boosting—CatBoost, Gaussian Process Regression—GPR, Extreme Gradient Boosting—XGBoost), and a white-box approach (Genetic Programming—GP), was employed to estimate crude oil residue at varying temperature intervals during TGA runs. Notably, the XGBoost model emerged as the most accurate, boasting a mean absolute percentage error (MAPE) of 0.7796% and a determination coefficient (R2) of 0.9999. Subsequently, the GPR, CatBoost, and GP models demonstrated commendable performance. The GP model, while displaying slightly higher error in comparison to the black-box models, yielded acceptable results and proved suitable for swift estimation of crude oil residue during pyrolysis. Furthermore, a sensitivity analysis was conducted to reveal the varying influence of input parameters on residual crude oil during pyrolysis. Among the inputs, temperature and asphaltenes were identified as the most influential factors in the crude oil pyrolysis process. Higher temperatures and oil °API gravity were associated with a negative impact, leading to a decrease in fuel deposition. On the other hand, increased values of asphaltenes, resins, and heating rates showed a positive impact, resulting in an increase in fuel deposition. These findings underscore the importance of precise modeling for fuel deposition during crude oil pyrolysis, offering insights that can significantly benefit ISC EOR practices.

Abstract When nanoparticles are dispersed and stabilized in a base-fluid, the resulting nanofluid undergoes considerable changes in its thermophysical properties, which can have a substantial influence on the performance of nanofluid-flow systems. With such necessity and importance, developing a set of mathematical correlations to identify these properties in various conditions can greatly eliminate costly and time-consuming experimental tests. Hence, the current study aims to develop innovative correlations for estimating the specific heat capacity of mono-nanofluids. The accurate estimation of this crucial property can result in the development of more efficient and effective thermal systems, such as heat exchangers, solar collectors, microchannel cooling systems, etc. In this regard, four powerful soft-computing techniques were considered, including Generalized Reduced Gradient (GRG), Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH). These techniques were implemented on 2084 experimental data-points, corresponding to ten different kinds of nanoparticles and six different kinds of base-fluids, collected from previous research sources. Eventually, four distinct correlations with high accuracy were provided, and their outputs were compared to three correlations that had previously been published by other researchers. These novel correlations are applicable to various oxide-based mono-nanofluids for a broad range of independent variable values. The superiority of newly developed correlations was proven through various statistical and graphical error analyses. The GMDH-based correlation revealed the best performance with an Average Absolute Percent Relative Error (AAPRE) of 2.4163% and a Coefficient of Determination (R2) of 0.9743. At last, a leverage statistical approach was employed to identify the GMDH technique’s application domain and outlier data, and also, a sensitivity analysis was carried out to clarify the degree of dependence between input and output variables.

Abstract Evaluation, prediction, and measurement of carbon dioxide (CO2) solubility in different polymers are crucial for engineers in various chemical applications, such as extraction and generation of novel materials. In this paper, correlations based on gene expression programming (GEP) were generated to predict the value of carbon dioxide solubility in three polymers. Results showed that the generated correlations could represent an outstanding efficiency and provide predictions for carbon dioxide solubility with satisfactory average absolute relative errors of 9.71%, 5.87%, and 1.63% for polystyrene (PS), polybutylene succinate-co-adipate (PBSA), and polybutylene succinate (PBS), respectively. Trend analysis based on Henry’s law illustrated that increasing pressure and decreasing temperature lead to an increase in carbon dioxide solubility. Finally, outlier discovery was applied using the leverage approach to detect the suspected data points. The outlier detection demonstrated the statistical validity of the developed correlations. William’s plot of three generated correlations showed that all of the data points are located in the valid zone except one point for PBS polymer and three points for PS polymer.

Abstract Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.

Abstract Interfacial tension (IFT) between surfactants and hydrocarbon is one of the important parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) operation. Measuring IFT in the laboratory is time-consuming and costly. Since, the accurate estimation of IFT is of paramount significance, modeling with advanced intelligent techniques has been used as a proper alternative in recent years. In this study, the IFT values between surfactants and hydrocarbon were predicted using tree-based machine learning algorithms. Decision tree (DT), extra trees (ET), and gradient boosted regression trees (GBRT) were used to predict this parameter. For this purpose, 390 experimental data collected from previous studies were used to implement intelligent models. Temperature, normal alkane molecular weight, surfactant concentration, hydrophilic–lipophilic balance (HLB), and phase inversion temperature (PIT) were selected as inputs of models and independent variables. Also, the IFT between the surfactant solution and normal alkanes was selected as the output of the models and the dependent variable. Moreover, the implemented models were evaluated using statistical analyses and applied graphical methods. The results showed that DT, ET, and GBRT could predict the data with average absolute relative error values of 4.12%, 3.52%, and 2.71%, respectively. The R-squared of all implementation models is higher than 0.98, and for the best model, GBRT, it is 0.9939. Furthermore, sensitivity analysis using the Pearson approach was utilized to detect correlation coefficients of the input parameters. Based on this technique, the results of sensitivity analysis demonstrated that PIT, surfactant concentration, and HLB had the greatest effect on IFT, respectively. Finally, GBRT was statistically credited by the Leverage approach.

Abstract Formation damage is a well-known problem that occurs during the exploration and production phases of the upstream sector of the oil and gas industry. This study aimed to develop a new drilling mud formulation by utilizing eco-friendly bio-polymers, specifically Carboxymethyl Cellulose (CMC), along with nanostructured materials and a common surfactant, sodium dodecyl sulfate (SDS). The rheological properties of the drilling fluid and the impact of additives on its properties were investigated at the micromodel scale, using a flow rate of 20 mL/h. The polymer concentration and nano clay concentration were set at two levels: 0.5 wt% and 1 wt%, respectively, while the surfactant content was varied at three levels: 0.1 wt%, 0.4 wt%, and 0.8 wt%. The results of the interfacial tension (IFT) analysis demonstrated a significant decrease in the interfacial tension between oil and water with the increasing concentration of SDS. Furthermore, following the API standard, the rheological behavior of the drilling fluid, including the gel strength and thixotropic properties of the mud, was evaluated with respect to temperature changes, as this is crucial for ensuring the inherent rheological stability of the mud. The rheological analysis indicated that the viscosity of the mud formulation with nanoparticles experienced a reduction of up to 10 times with increasing shear rate, while other formulations exhibited a decline of 100 times. Notably, the rheological properties of the Agar specimen improved at 150 °F due to its complete solubility in water, whereas other formulations exhibited a greater drop in viscosity at this temperature. As the temperature increased, drilling fluid containing nanostructured materials exhibited higher viscosity.

Abstract Ionic liquids (ILs) have drawn much attention due to their extensive applications and environment-friendly nature. Refractive index prediction is valuable for ILs quality control and property characterization. This paper aims to predict refractive indices of pure ILs and identify factors influencing refractive index changes. Six chemical structure-based machine learning models called eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Network (CNN), Adaptive Boosting-Decision Tree (Ada-DT), and Adaptive Boosting-Support Vector Machine (Ada-SVM) were developed to achieve this goal. An enormous dataset containing 6098 data points of 483 different ILs was exploited to train the machine learning models. Each data point’s chemical substructures, temperature, and wavelength were considered for the models’ inputs. Including wavelength as input is unprecedented among predictions done by machine learning methods. The results show that the best model was CatBoost, followed by XGBoost, LightGBM, Ada-DT, CNN, and Ada-SVM. The R2 and average absolute percent relative error (AAPRE) of the best model were 0.9973 and 0.0545, respectively. Comparing this study’s models with the literature shows two advantages regarding the dataset’s abundance and prediction accuracy. This study also reveals that the presence of the –F substructure in an ionic liquid has the most influence on its refractive index among all inputs. It was also found that the refractive index of imidazolium-based ILs increases with increasing alkyl chain length. In conclusion, chemical structure-based machine learning methods provide promising insights into predicting the refractive index of ILs in terms of accuracy and comprehensiveness.

Abstract In gas-condensate reservoirs, liquid dropout occurs by reducing the pressure below the dew point pressure in the area near the wellbore. Estimation of production rate in these reservoirs is important. This goal is possible if the amount of viscosity of the liquids released below the dew point is available. In this study, the most comprehensive database related to the viscosity of gas condensate, including 1370 laboratory data was used. Several intelligent techniques, including Ensemble methods, support vector regression (SVR), K-nearest neighbors (KNN), Radial basis function (RBF), and Multilayer Perceptron (MLP) optimized by Bayesian Regularization and Levenberg–Marquardt were applied for modeling. In models presented in the literature, one of the input parameters for the development of the models is solution gas oil ratio (Rs). Measuring Rs in wellhead requires special equipment and is somewhat difficult. Also, measuring this parameter in the laboratory requires spending time and money. According to the mentioned cases, in this research, unlike the research done in the literature, Rs parameter was not used to develop the models. The input parameters for the development of the models presented in this research were temperature, pressure and condensate composition. The data used includes a wide range of temperature and pressure, and the models presented in this research are the most accurate models to date for predicting the condensate viscosity. Using the mentioned intelligent approaches, precise compositional models were presented to predict the viscosity of gas/condensate at different temperatures and pressures for different gas components. Ensemble method with an average absolute percent relative error (AAPRE) of 4.83% was obtained as the most accurate model. Moreover, the AAPRE values for SVR, KNN, MLP-BR, MLP-LM, and RBF models developed in this study are 4.95%, 5.45%, 6.56%, 7.89%, and 10.9%, respectively. Then, the effect of input parameters on the viscosity of the condensate was determined by the relevancy factor using the results of the Ensemble methods. The most negative and positive effects of parameters on the gas condensate viscosity were related to the reservoir temperature and the mole fraction of C11, respectively. Finally, suspicious laboratory data were determined and reported using the leverage technique.

Abstract In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H2S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H2S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H2S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (Tc) and pressure (Pc), acentric factor (ω), boiling temperature (Tb), and molecular weight (Mw), were used in these models; the output was the solubility of H2S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R2) of 0.99, provides more precise calculations for H2S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H2S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H2S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H2S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H2S.

Abstract Decreasing the conventional sources of oil reservoirs attracts researchers’ attention to the tertiary recovery of oil reservoirs, such as in-situ catalytic upgrading. In this contribution, the response surface methodology (RSM) approach and multi-objective optimization were utilized to investigate the effect of reaction temperature and catalysts soaking time on the concentration distribution of upgraded oil samples. To this end, 22 sets of experimental oil upgrading over Ni–W–Mo catalyst were utilized for the statistical modeling. Then, optimization based on the minimum reaction temperature, catalysts soaking time, gas, and residue wt.% was performed. Also, correlations for the prediction of concentration of different fractions (residue, vacuum gas oil (VGO), distillate, naphtha, and gases) as a function of independent factors were developed. Statistical results revealed that RSM model is in good agreement with experimental data and high coefficients of determination (R2 = 0.96, 0.945, 0.97, 0.996, 0.89) are the witness for this claim. Finally, based on multi-objective optimization, 378.81 °C and 17.31 h were obtained as the optimum upgrading condition. In this condition, the composition of residue, VGO, distillate, naphtha, and gases are 6.798%, 39.23%, 32.93%, 16.865%, and 2.896%, respectively, and the optimum condition is worthwhile for the pilot and industrial application of catalyst injection during in-situ oil upgrading.

Abstract Asphaltene precipitation and its adsorption on different surfaces are challenging topics in the upstream and downstream of the oil industries and the environment. In this research, the phenomenon of asphaltenes adsorption in the presence and absence of water on the surface of magnetite, hematite, calcite, and dolomite nanoparticles (NPs) was investigated. Five asphaltenes of different origins, four NPs as adsorbents and Persian Gulf water were used for three-phase (asphaltene/toluene solution + NPs + water) experiments. Characterization of asphaltenes and NPs was performed using Fourier transform infrared spectroscopic (FTIR), dynamic light scattering (DLS), elemental analysis, and field emission scanning electron microscopy (FESEM). Adsorption experiments were performed in two- (asphaltene/toluene solution + NPs) and three-phase systems. The results showed that the most effective parameters for asphaltene adsorption onto these NPs are the asphaltene composition, namely nitrogen content, and the aromaticity of asphaltenes. The significant effects of these parameters were also confirmed by the relevancy factor function as a sensitivity analysis. In the competition of asphaltene adsorption capacity by NPs, iron oxide NPs had the highest adsorption (Magnetite NPs > Hematite NPs > Calcite NPs > Dolomite NPs). From the results of the experiments in the presence of water phase, it could be pointed out that the asphaltenes adsorption onto the NPs was accompanied by a decrease compared to the experiments in the absence of water. The modeling also showed that physical adsorption has a significant contribution to the asphaltenes adsorption on the surface of iron oxides and lime NPs. The results of this research can assist in a better understanding of the asphaltene adsorption phenomenon and the role of iron oxide and lime NPs in solving this problem.

Asphaltene precipitation/deposition is one of the main problems in petroleum industry at various stages. In this study, various controlling factors on the adsorption of different types of asphaltene on the surface of dolomite of different size, as one of the most abundant minerals in the reservoir is investigated. First, asphaltenes and adsorbents (dolomite) properties were analyzed via several analytical techniques. Then, adsorption of four asphaltene samples of various origins and chemical composition on dolomite at micron and nano size were carried out in both static and dynamic modes and results were examined following the adsorption process. Based on the static tests, the adsorption amount of asphaltenes on nano- and micro-dolomite was measured 3.52–11 mg/m2 and 0.82–2.74 mg/m2, respectively. The results revealed that the most significant parameters that would affect the asphaltene adsorption on dolomite as an adsorbent are the total nitrogen and sulfur (N + S) content and the aromaticity of the asphaltenes. Moreover, as the flow rate was increased three times in the dynamic experiments, a decrease of 15–25% in asphaltene adsorption was observed, which was attributed to physisorption of asphaltene on dolomite or due to higher flow velocities. Collectively, the findings of this study can enable us to better understand the mechanisms of asphaltene adsorption/precipitation on dolomite and lead to better management of oil production from dolomitic reservoirs.

Gas injection has emerged over the recent decades as a promising technology to enhance oil recovery in various fields worldwide. The efficiency and success of a gas injection operation can be assessed through a number of vital experimental studies. Interfacial Tension (IFT) between the injected gas and the displacing fluid is a key parameter playing an eminent role in the foregoing studies. The main scope of this work is making a progress in modeling the IFTs between diverse n-alkanes and Methane (CH4), Carbon Dioxide (CO2), and Nitrogen (N2) natural gases. For this purpose, two smart AI-based approaches of Cascaded Feedforward Neural Network (CFNN) and Decision Tree Learning (DT) were used to simultaneously model the IFTs between foregoing immiscible binary systems as a function of pressure, temperature, the gases properties, and the properties of the liquid. Several statistical measures and graphical descriptions were employed to aid the accuracy analysis of the proposed models. Both developed CFNN and DT networks represented desirable close-to-reality predictions in all binary systems. Besides, CFNN established itself as the most robust model for all studies binary systems with RMSE values of 0.5924, 0.5649, and 0.5870 mN/m, and R2 values of 0.9902, 0.9910, and 0.9904 for the train, test, and overall data, respectively.

Surfactant-polymer flooding is one of the most important enhanced oil recovery (EOR) techniques, which refers to the injection of surfactant slugs and polymer drives. Two crucial decision-making parameters in EOR operations are net present value (NPV) and oil recovery factor (RF). Herein, various intelligent models, based on multilayer perceptron (MLP), cascade neural network (CNN), radial basis function (RBF), neural networks as well as support vector regression (SVR), and decision tree (DT) algorithms are proposed toward estimating these two parameters with respect to polymer drive size, surfactant slug size, the salinity of polymer drive, Kv/Kh ratio, surfactant concentration, and polymer concentration in polymer drive and surfactant slug. The results exhibited the outperformance of the CNN model trained with the Levenberg Marquardt algorithm in forecasting the RF and NPV with average absolute errors of 0.66% and 1.95%, respectively. Moreover, the results of the sensitivity analysis reflected that the most effective inputs on the predicted value of RF were surfactant concentration and surfactant slug size, while surfactant concentration and polymer concentration in surfactant slug could considerably affect the NPV model’s output. Lastly, the outlier detection analysis revealed that the employed data is valid and only two points were detected as outliers.

Abstract Corrosion protection of metals is of paramount importance in different sectors of industry. One of the emerging techniques to prevent or reduce the damaging effects of this phenomenon is to apply superhydrophobic coatings on the susceptible surfaces. In this study, corrosion protection of steel is investigated by fabricating superhydrophobic coatings, using one-step electrodeposition process of nanosilica hybrid film and spraying process of polytetrafluoroethylene (PTFE) on steel surface and also preparation of micro/nano-composite coatings. The anti-corrosion behavior of the nanosilica hybrid film and PTFE coating with two types of microparticles including Al2O3 powder and glass beads in primer layer, and overcoat layer with and without SiO2 nanoparticles is studied. TOEFL polarization and electrochemical impedance spectroscopy (EIS) tests are conducted on coated steel samples to examine their corrosion performance in 3.5 wt% NaCl solution at a temperature of 25 °C. The results showed that the combination of superhydrophobic properties and low conductivity significantly improves the corrosion resistance. Evaluating the effect of adding SiO2 nanoparticles to the overcoat layer in PTFE coating showed that the nanoparticles improve the corrosion resistance of PTFE coatings by sealing up some defects and pores in the coating. Investigation of corrosion resistance of coatings showed that, the corrosion resistance of nanosilica film is lower than that of PTFE coatings. The best sample obtained in this study, namely the PTFE coating with glass beads microparticles in primer layer and SiO2 nanoparticles in overcoat layer, reduced the corrosion rate by nearly 80 times.

Abstract Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

Abstract Ionic liquids (ILs) have emerged as suitable options for gas storage applications over the past decade. Consequently, accurate prediction of gas solubility in ILs is crucial for their application in the industry. In this study, four intelligent techniques including Extreme Learning Machine (ELM), Deep Belief Network (DBN), Multivariate Adaptive Regression Splines (MARS), and Boosting-Support Vector Regression (Boost-SVR) have been proposed to estimate the solubility of some gaseous hydrocarbons in ILs based on two distinct methods. In the first method, the thermodynamic properties of hydrocarbons and ILs were used as input parameters, while in the second method, the chemical structure of ILs and hydrocarbons along with temperature and pressure were used. The results show that in the first method, the DBN model with root mean square error (RMSE) and coefficient of determination (R2) values of 0.0054 and 0.9961, respectively, and in the second method, the DBN model with RMSE and R2 values of 0.0065 and 0.9943, respectively, have the most accurate predictions. To evaluate the performance of intelligent models, the obtained results were compared with previous studies and equations of the state including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Redlich–Kwong (RK), and Zudkevitch–Joffe (ZJ). Findings show that intelligent models have high accuracy compared to equations of state. Finally, the investigation of the effect of different factors such as alkyl chain length, type of anion and cation, pressure, temperature, and type of hydrocarbon on the solubility of gaseous hydrocarbons in ILs shows that pressure and temperature have a direct and inverse effect on increasing the solubility of gaseous hydrocarbons in ILs, respectively. Also, the evaluation of the effect of hydrocarbon type shows that increasing the molecular weight of hydrocarbons increases the solubility of gaseous hydrocarbons in ILs.

Abstract One of the most important problems that the drilling industry faces is drilling cost. Many factors affect the cost of drilling. Increasing drilling time has a significant role in increasing drilling costs. One of the solutions to reduce drilling time is to optimize the drilling rate. Drilling wells at the optimum time will reduce the time and thus reduce the cost of drilling. The drilling rate depends on different factors, some of which are controllable and some are uncontrollable. In this study, several smart models and a correlation were proposed to predict the rate of penetration (ROP) which is very important for planning a drilling operation. 5040 real data points from a field in the South of Iran have been used. The ROP was modelled using Radial Basis Function, Decision Tree (DT), Least Square Vector Machine (LSSVM), and Multilayer Perceptron (MLP). Bayesian Regularization Algorithm (BRA), Scaled Conjugate Gradient Algorithm and Levenberg–Marquardt Algorithm were employed to train MLP and Gradient Boosting (GB) was used for DT. To evaluate the accuracy of the developed models, both graphical and statistical techniques were used. The results showed that DT-GB model with an R2 of 0.977, has the best performance, followed by LSSVM and MLP-BRA with R2 of 0.971 and 0.969, respectively. Aside from that, the proposed empirical correlation has an acceptable accuracy in spite of simplicity. Moreover, sensitivity analysis illustrated that depth and pump pressure have the highest effects on ROP. In addition, the leverage approach approved that the developed DT-GB model is valid statistically and about 1% of the data are suspected or out of the applicability domain of the model.

Abstract The need to determine permeability at different stages of evaluation, completion, optimization of Enhanced Oil Recovery (EOR) operations, and reservoir modeling and management is reflected. Therefore, various methods with distinct efficiency for the evaluation of permeability have been proposed by engineers and petroleum geologists. The oil industry uses acoustic and Nuclear Magnetic Resonance (NMR) loggings extensively to determine permeability quantitatively. However, because the number of available NMR logs is not enough and there is a significant difficulty in their interpreting and evaluation, the use of acoustic logs to determine the permeability has become very important. Direct, continuous, and in-reservoir condition estimation of permeability is a unique feature of the Stoneley waves analysis as an acoustic technique. In this study, five intelligent mathematical methods, including Adaptive Network-Based Fuzzy Inference System (ANFIS), Least-Square Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), Multi-Layer Perceptron Neural Network (MLPNN), and Committee Machine Intelligent System (CMIS), have been performed for calculating permeability in terms of Stoneley and shear waves travel-time, effective porosity, bulk density and lithological data in one of the naturally-fractured and low-porosity carbonate reservoirs located in the Southwest of Iran. Intelligent models have been improved with three popular optimization algorithms, including Coupled Simulated Annealing (CSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Among the developed models, the CMIS is the most accurate intelligent model for permeability forecast as compared to the core permeability data with a determination coefficient (R2) of 0.87 and an average absolute deviation (AAD) of 3.7. Comparing the CMIS method with the NMR techniques (i.e., Timur-Coates and Schlumberger-Doll-Research (SDR)), the superiority of the Stoneley method is demonstrated. With this model, diverse types of fractures in carbonate formations can be easily identified. As a result, it can be claimed that the models presented in this study are of great value to petrophysicists and petroleum engineers working on reservoir simulation and well completion.

Abstract Shear sonic wave velocity (Vs) has a wide variety of implications, from reservoir management and development to geomechanical and geophysical studies. In the current study, two approaches were adopted to predict shear sonic wave velocities (Vs) from several petrophysical well logs, including gamma ray (GR), density (RHOB), neutron (NPHI), and compressional sonic wave velocity (Vp). For this purpose, five intelligent models of random forest (RF), extra tree (ET), Gaussian process regression (GPR), and the integration of adaptive neuro fuzzy inference system (ANFIS) with differential evolution (DE) and imperialist competitive algorithm (ICA) optimizers were implemented. In the first approach, the target was estimated based only on Vp, and the second scenario predicted Vs from the integration of Vp, GR, RHOB, and NPHI inputs. In each scenario, 8061 data points belonging to an oilfield located in the southwest of Iran were investigated. The ET model showed a lower average absolute percent relative error (AAPRE) compared to other models for both approaches. Considering the first approach in which the Vp was the only input, the obtained AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.54%, 1.34%, 1.54%, 1.56%, and 1.57%, respectively. In the second scenario, the achieved AAPRE values for RF, ET, GPR, ANFIS + DE, and ANFIS + ICA models are 1.25%, 1.03%, 1.16%, 1.63%, and 1.49%, respectively. The Williams plot proved the validity of both one-input and four-inputs ET model. Regarding the ET model constructed based on only one variable,Williams plot interestingly showed that all 8061 data points are valid data. Also, the outcome of the Leverage approach for the ET model designed with four inputs highlighted that there are only 240 “out of leverage” data sets. In addition, only 169 data are suspected. Also, the sensitivity analysis results typified that the Vp has a higher effect on the target parameter (Vs) than other implemented inputs. Overall, the second scenario demonstrated more satisfactory Vs predictions due to the lower obtained errors of its developed models. Finally, the two ET models with the linear regression model, which is of high interest to the industry, were applied to diagnose candidate layers along the formation for hydraulic fracturing. While the linear regression model fails to accurately trace variations of rock properties, the intelligent models successfully detect brittle intervals consistent with field measurements.

Abstract Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO2) and nitrogen (N2) in water and brine is one of the most controversial challenges in the oil and chemical industries. Although many researches have been conducted on solubility of gases in brine and water, very few researches investigated the solubility of power plant flue gases (CO2–N2 mixtures) in aqueous solutions. In this study, using six intelligent models, including Random Forest, Decision Tree (DT), Gradient Boosting-Decision Tree (GB-DT), Adaptive Boosting-Decision Tree (AdaBoost-DT), Adaptive Boosting-Support Vector Regression (AdaBoost-SVR), and Gradient Boosting-Support Vector Regression (GB-SVR), the solubility of CO2–N2 mixtures in water and brine solutions was predicted, and the results were compared with four equations of state (EOSs), including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Valderrama–Patel–Teja (VPT), and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT). The results indicate that the Random Forest model with an average absolute percent relative error (AAPRE) value of 2.8% has the best predictions. The GB-SVR and DT models also have good precision with AAPRE values of 6.43% and 7.41%, respectively. For solubility of CO2 present in gaseous mixtures in aqueous systems, the PC-SAFT model, and for solubility of N2, the VPT EOS had the best results among the EOSs. Also, the sensitivity analysis of input parameters showed that increasing the mole percent of CO2 in gaseous phase, temperature, pressure, and decreasing the ionic strength increase the solubility of CO2–N2 mixture in water and brine solutions. Another significant issue is that increasing the salinity of brine also has a subtractive effect on the solubility of CO2–N2 mixture. Finally, the Leverage method proved that the actual data are of excellent quality and the Random Forest approach is quite reliable for determining the solubility of the CO2–N2 gas mixtures in aqueous systems.

Abstract Accurate prediction of the solubility of gases in hydrocarbons is a crucial factor in designing enhanced oil recovery (EOR) operations by gas injection as well as separation, and chemical reaction processes in a petroleum refinery. In this work, nitrogen (N2) solubility in normal alkanes as the major constituents of crude oil was modeled using five representative machine learning (ML) models namely gradient boosting with categorical features support (CatBoost), random forest, light gradient boosting machine (LightGBM), k-nearest neighbors (k-NN), and extreme gradient boosting (XGBoost). A large solubility databank containing 1982 data points was utilized to establish the models for predicting N2 solubility in normal alkanes as a function of pressure, temperature, and molecular weight of normal alkanes over broad ranges of operating pressure (0.0212–69.12 MPa) and temperature (91–703 K). The molecular weight range of normal alkanes was from 16 to 507 g/mol. Also, five equations of state (EOSs) including Redlich–Kwong (RK), Soave–Redlich–Kwong (SRK), Zudkevitch–Joffe (ZJ), Peng–Robinson (PR), and perturbed-chain statistical associating fluid theory (PC-SAFT) were used comparatively with the ML models to estimate N2 solubility in normal alkanes. Results revealed that the CatBoost model is the most precise model in this work with a root mean square error of 0.0147 and coefficient of determination of 0.9943. ZJ EOS also provided the best estimates for the N2 solubility in normal alkanes among the EOSs. Lastly, the results of relevancy factor analysis indicated that pressure has the greatest influence on N2 solubility in normal alkanes and the N2 solubility increases with increasing the molecular weight of normal alkanes.

Abstract In recent years, metal organic frameworks (MOFs) have been distinguished as a very promising and efficient group of materials which can be used in carbon capture and storage (CCS) projects. In the present study, the potential ability of modern and powerful decision tree-based methods such as Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) was investigated to predict carbon dioxide adsorption by 19 different MOFs. Reviewing the literature, a comprehensive databank was gathered including 1191 data points related to the adsorption capacity of different MOFs in various conditions. The inputs of the implemented models were selected as temperature (K), pressure (bar), specific surface area (m2/g) and pore volume (cm3/g) of the MOFs and the output was CO2 uptake capacity (mmol/g). Root mean square error (RMSE) values of 0.5682, 1.5712, 1.0853, and 1.9667 were obtained for XGBoost, CatBoost, LightGBM, and RF models, respectively. The sensitivity analysis showed that among all investigated parameters, only the temperature negatively impacts the CO2 adsorption capacity and the pressure and specific surface area of the MOFs had the most significant effects. Among all implemented models, the XGBoost was found to be the most trustable model. Moreover, this model showed well-fitting with experimental data in comparison with different isotherm models. The accurate prediction of CO2 adsorption capacity by MOFs using the XGBoost approach confirmed that it is capable of handling a wide range of data, cost-efficient and straightforward to apply in environmental applications.

Abstract Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries.

Abstract Inner coatings have emerged as a novel technique to prevent the deposition of paraffin, wax, scale, and corrosion of pipelines during oil production and transport. Few studies addressed this technique for preventing asphaltene deposition. In this study, two superhydrophobic inner coatings, including polytetrafluoroethylene (PTFE) coating and nanosilica coating, were fabricated on metal surfaces and the asphaltene deposition on these coated surfaces was examined. A model oil solution was prepared using asphaltene and heptol and the effect of static and dynamic flow states on the amount of asphaltene deposition on uncoated electrodes, PTFE coated electrodes, and nanosilica coated electrodes were investigated. The results showed that the PTFE coating is more effective in reducing asphaltene deposition than nanosilica coating. The PTFE coating could reduce 56% of the deposition in a static state and more than 70% in a dynamic state at an asphaltene concentration of 2000 ppm. For PTFE coating in a dynamic state, the deposition rate is negligible in long times. In addition, it was found that the type of flow state affects the asphaltene deposition kinetics. The results demonstrate that, in the static state, the nth-order kinetics model, and in the dynamic state, the double exponential models are in best agreement with the experimental data.

Viscosity is a crucial thermophysical feature of a substance that must be accurately determined before designing a system with nanofluid as the working fluid. In this study, the modern technique of committee machine intelligent system (CMIS) is used for establishing a predictive model for the relative viscosity of the water-based nanofluids. The model was developed by considering 1440 experimental data points of different types of water-based nanofluids containing Al2O3, SiC, SiO2, TiO2, CuO, nanodiamond, and Fe3O4 nanoparticles. The CMIS model combines three intelligent models including a multilayer perceptron (MLP) model trained with Levenberg-Marquardt (LM), an MLP model trained by Bayesian Regularization (BR) and a radial basis function (RBF) approach to estimate the relative viscosity of different water-based nanofluids. Statistical and graphical error criteria revealed that the CMIS technique successfully estimates the relative viscosity of all data points over the whole ranges of operational conditions with a mean absolute relative error of approximately 1.25%. According to their precision and performance, the established CMIS system provides the best performance, followed by the BR-MLP, LM-MLP, and RBF models. Moreover, the performance and estimation capability of the CMIS model was verified against 13 theoretical and empirical models.

Due to industrial development, the volume of carbon dioxide (CO2) is rapidly increasing.. Several techniques have been used to eliminate CO2 from the output gas mixtures. One of these methods is CO2 capturing by ionic liquids (ILs). Computational models for estimating the CO2 solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO2 solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO2 in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (Tc), critical pressure (Pc) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO2 solubility estimation in ILs and is capable of being used in different industries.

The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model’s superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity.

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil–water systems (Krw and Kro) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on Krw and Kro is still a major challenge for precise studying of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable and simple to use paradigms to predict the temperature dependency of Krw and Kro. To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for Krw and Kro, respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of Krw and Kro in TEOR.

Carbon dioxide-based enhanced oil-recovery (CO2-EOR) processes have gained considerable interest among other EOR methods. In this paper, based on the molecular weight of paraffins (n-alkanes), pressure, and temperature, the magnitude of CO2–n-alkanes interfacial tension (IFT) was determined by utilizing soft computing and mathematical modeling approaches, namely: (i) radial basis function (RBF) neural network (optimized by genetic algorithm (GA), gravitational search algorithm (GSA), imperialist competitive algorithm (ICA), particle swarm optimization (PSO), and ant colony optimization (ACO)), (ii) multilayer perception (MLP) neural network (optimized by Levenberg-Marquardt (LM)), and (iii) group method of data handling (GMDH). To do so, a broad range of laboratory data consisting of 879 data points collected from the literature was employed to develop the models. The proposed RBF-ICA model, with an average absolute percent relative error (AAPRE) of 4.42%, led to the most reliable predictions. Furthermore, the Parachor approach with different scaling exponents (n) in combination with seven equations of state (EOSs) was applied for IFT predictions of the CO2–n-heptane and CO2–n-decane systems. It was found that n = 4 was the optimum value to obtain precise IFT estimations; and combinations of the Parachor model with three-parameter Peng–Robinson and Soave–Redlich–Kwong EOSs could better estimate the IFT of the CO2–n-alkane systems, compared to other used EOSs.

Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.