Modeling viscosity of crude oil using k-nearest neighbor algorithm

Oil viscosity is an important factor in every project of the petroleum industry. These processes can range from gas injection to oil reservoirs to comprehensive reservoir simulation studies. Different experimental approaches have been proposed for measuring oil viscosity. However, these methods are often time taking, cumbersome and at some physical conditions, impossible. Therefore, development of predictive models for estimating this parameter is crucial. In this study, three new machine learning based models are developed to estimate the oil viscosity. These approaches are genetic programing, k-nearest neighbor (KNN) and linear discriminant analysis. Oil gravity and temperature were the input parameters of the models. Various graphical and statistical error analyses were used to measure the performance of the developed models. Also, comparison study between the developed models and the well-known previously published models was conducted. Moreover, trend analysis was performed to compare the predictions of the models with the trend of experimental data. The results indicated that the developed models outperform all of the previously published models by showing negligible prediction errors. Among the developed models, the KNN model has the highest accuracy by showing an overall mean absolute error of 8.54%. The results show that the new developed models in this study can be potentially utilized in reservoir simulation packages of the petroleum industry.Cited as: Mahdiani, M.R., Khamehchi, E., Hajirezaie, S., Hemmati-Sarapardeh, A. Modeling viscosity of crude oil using k-nearest neighbor algorithm. Advances in Geo-Energy Research, 2020, 4(4): 435-447, doi: 10.46690/ager.2020.04.0

An advanced computational intelligent framework to predict shear sonic velocity with application to mechanical rock classification

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

Application of nanofluids for treating fines migration during hydraulic fracturing: Experimental study and mechanistic understanding

 Hydraulic fracturing has emerged as one of the best and most economic methods for enhancing oil recovery from low permeability reservoirs such as shale gas reservoirs. However, its performance will be negatively affected by fines migration due to hydraulic fracturing process. In the present study, it has been tried to experimentally investigate the efficiency of a synthesized Nanosilica particles in reducing fines migration for the first time in literature. To this end, two sets of static and dynamic experiments, namely glass bead funnel test and core displacement analysis, were implemented, respectively. In the static test, increasing the soaking time and addition of Nanosilica led to the clearer effluent fluid, resulting in less concentrations of clay particles in solution. When the mixture of Nanosilica and glass beads was available in the solution, a higher differential pressure was obtained during dynamic condition in comparison to only glass beads, which means the lower permeability of the porous media. Moreover, DLVO theory was applied to demonstrate the clay particles absorption on the sand proppants surfaces.  Consequently, it was observed that the use of Nanosilica particles mixed with sand proppant can effectively reduce fines migration; thereby, it can enhance hydraulic performance of the fracturing operation.Cited as: Moghadasi, R., Rostami, A., Hemmati-Sarapardeh, A. Application of nanofluids for treating fines migration during hydraulic fracturing: Experimental study and mechanistic understanding. Advances in Geo-Energy Research, 2019, 3(2): 198-206, doi: 10.26804/ager.2019.02.0

Application of nanofluids for treating fines migration during hydraulic fracturing: Experimental study and mechanistic understanding

Hydraulic fracturing has emerged as one of the best and most economic methods for enhancing oil recovery from low permeability reservoirs such as shale gas reservoirs. However, its performance will be negatively affected by fines migration due to hydraulic fracturing process. In the present study, it has been tried to experimentally investigate the efficiency of a synthesized Nanosilica particles in reducing fines migration for the first time in literature. To this end, two sets of static and dynamic experiments, namely glass bead funnel test and core displacement analysis, were implemented, respectively. In the static test, increasing the soaking time and addition of Nanosilica led to the clearer effluent fluid, resulting in less concentrations of clay particles in solution. When the mixture of Nanosilica and glass beads was available in the solution, a higher differential pressure was obtained during dynamic condition in comparison to only glass beads, which means the lower permeability of the porous media. Moreover, DLVO theory was applied to demonstrate the clay particles absorption on the sand proppants surfaces. Consequently, it was observed that the use of Nanosilica particles mixed with sand proppant can effectively reduce fines migration; thereby, it can enhance hydraulic performance of the fracturing operation

Modeling of carbon dioxide solubility in ionic liquids based on group method of data handling

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

Evaluation of asphaltene adsorption on minerals of dolomite and sandstone formations in two and three-phase systems

Asphaltene adsorption on reservoir rock minerals causes wettability alteration and pore plugging which subsequently reduces crude oil production. Also, it has a negative effect on the efficiency of production and enhanced oil recovery operations. In this study, the adsorption of extracted asphaltenes of two samples of Iranian oil fields on dolomite, quartz, and magnetite was investigated in two-and three-phase systems in both static and dynamic flow modes. Mineral adsorbents were analyzed by Brunauer–Emmett–Teller and X-ray fluorescence methods. Also, several laboratory tests including elemental analysis, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy were carried out to characterize asphaltenes. The results showed that in addition to the effect of known parameters such as asphaltenes concentration and specific surface area of the solid phase, the water phase also affects the amount of asphaltenes adsorption. The adsorption amount of asphaltenes increases with increasing the specific surface area of adsorbent (decreasing particle size) and increasing the initial concentration of asphaltenes, and decreases with the addition of water to the two-phase system. The static adsorption amount of asphaltenes in a two-phase system can be up to 90% higher than the adsorption amount in a three-phase system. Doubling the fluid flow rate in dynamic adsorption significantly (by about 20%) reduces the asphaltenes adsorption, which could be a sign of physical adsorption of asphaltenes on adsorbents. The structure and elemental composition of asphaltenes also have a significant effect on asphaltenes adsorption. The asphaltene sample, which had a more aromatic nature and high nitrogen content, had higher adsorption on reservoir rock minerals. Finally, fitting the adsorption equilibrium models with experimental data reveals that the adsorption isotherm model depends on the type and particle size of the adsorbents and the concentration and type of asphaltenes.Cited as: Mohammadi, M.R., Bahmaninia, H., Ansari, S., Hemmati-Sarapardeh, A., Norouzi-Apourvari, S., Schaffie, M., Ranjbar, M. Evaluation of asphaltene adsorption on minerals of dolomite and sandstone formations in two and three-phase systems. Advances in Geo-Energy Research, 2021, 5(1), 39-52, doi: 10.46690/ager.2021.01.0

A review on zeolitic imidazolate frameworks use for crude oil spills cleanup

 Oil spills are a global concern by virtue of their distractive effects on the ecosystem. Many studies have examined the use of porous materials as sorbents for contaminants from different polluted waters. For example, hydrophobic metal organic frameworks, especially zeolitic imidazolate frameworks (ZIFs) with high porosity, have attracted lots of attention. ZIFs are a subclass of metal organic frameworks and display an excellent performance toward oil/water separation compared with other porous materials. Nevertheless, the performance of ZIFs toward oil spills cleanup has not been reviewed. Accordingly, this article overviews the different methods for ZIF preparation, their corresponding structure, and their various applications as sorbents and in particular, recent developments in cleaning up oil spills with meso and micro-porous ZIFs. The investigation of the literatures revealed that the effective parameters on the performance of porous ZIFs are specific surface area, pore diameters of ZIF, and the size of cavities due to interconnecting of ZIF particles. The ZIF-8 with a high surface area of 1408 m2/g and 1384.2 m2/g and adsorption capacity up to 3000 mg/g was studied more than the other ZIF structures. Models predications revealed the maximum adsorption capacity of 6633 mg/g for ZIF-8. Recently, investigations focused on carbonitride foam and melamine sponge as templates of ZIF powder. In comparison with synthesis methods, dip coating as a facial synthesis method was introduced for production and anchoring ZIF particles on the substrate. The recyclability of crude oil and the reusability of the ZIF sorbents are highlighted. Moreover, this article reviews recent developments of ZIFs synthesis, current challenges, and prospects for the use of ZIFs in oil/water separation. The findings of this study can help to better understand widespread applications of ZIFs, effective features of a sorbent, and methods to improve adsorption capacity. As cleaning up oil spills is known as an important issue, this is the first study on ZIFs in particular oil/water separation which provides a summary of researches in a simple form along with recent developments compared to published reviews.Cited as: Shahmirzaee, M., Hemmati-Sarapardeh, A., Husein, M.M., Schaffifie, M., Ranjbar, M. A review on zeolitic imidazolate frameworks use for crude oil spills cleanup. Advances in Geo-Energy Research, 2019, 3(3): 320-342, doi: 10.26804/ager.2019.03.1

Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

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

Modeling temperature dependency of oil-water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil–water systems (K[sub: rw] and K[sub: ro]) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on K[sub: rw] and K[sub: ro] 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 K[sub: rw] and K[sub: ro]. 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 K[sub: rw] and K[sub: ro], 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 K[sub: rw] and K[sub: ro] in TEOR

A CSA-LSSVM model to estimate diluted heavy oil viscosity in the presence of kerosene

Viscosity is one of the properties that has important role in enhanced oil recovery processes, simulating reservoirs, and designing production facilities. Therefore, measurement and calculation of its accurate value is worthwhile. While the experimental methods for measurement of viscosity are expensive and time consuming, some credible correlations were developed to predict the viscosity with enough accuracy. For this purpose, in this study a balky data bank was gathered from open literature sources, and then one machine learning based approach called least square support vector machine (LSSVM) was utilized for prediction of heavy and extra-heavy crude oil viscosity. The parameters of proposed model were optimized by couple simulated annealing (CSA) optimization approach. The inputs of this model are temperature and kerosene mass fraction and the only output is viscosity