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Open Access
SPONTANEOUS IMBIBITION OIL RECOVERY BY NATURAL SURFACTANT/NANOFLUID: AN EXPERIMENTAL AND THEORETICAL STUDY
(Nanomaterials, 2022) Khoramian, Reza; Kharrat, Riyaz; Pourafshary, Peyman; Golshokooh, Saeed; Hashemi, Fatemeh
Organic surfactants have been utilized with different nanoparticles in enhanced oil recovery (EOR) operations due to the synergic mechanisms of nanofluid stabilization, wettability alteration, and oil-water interfacial tension reduction. However, investment and environmental issues are the main concerns to make the operation more practical. The present study introduces a natural and cost-effective surfactant named Azarboo for modifying the surface traits of silica nanoparticles for more efficient EOR. Surface-modified nanoparticles were synthesized by conjugating negatively charged Azarboo surfactant on positively charged amino-treated silica nanoparticles. The effect of the hybrid application of the natural surfactant and amine-modified silica nanoparticles was investigated by analysis of wettability alteration. Amine-surfactant-functionalized silica nanoparticles were found to be more effective than typical nanoparticles. Amott cell experiments showed maximum imbibition oil recovery after nine days of treatment with amine-surfactant-modified nanoparticles and fifteen days of treatment with amine-modified nanoparticles. This finding confirmed the superior potential of amine-surfactant-modified silica nanoparticles compared to amine-modified silica nanoparticles. Modeling showed that amine surfactant-treated SiO2 could change wettability from strongly oil-wet to almost strongly water-wet. In the case of amine-treated silica nanoparticles, a strongly water-wet condition was not achieved. Oil displacement experiments confirmed the better performance of aminesurfactant- treated SiO2 nanoparticles compared to amine-treated SiO2 by improving oil recovery by 15%. Overall, a synergistic effect between Azarboo surfactant and amine-modified silica nanoparticles led to wettability alteration and higher oil recovery.
Open Access
REVISITED BAYESIAN SEQUENTIAL INDICATOR SIMULATION: USING A LOG-LINEAR POOLING APPROACH
(Mathematics, 2022) Madani, Nasser
It has been more than a decade since sequential indicator simulation was proposed to model geological features. Due to its simplicity and easiness of implementation, the algorithm attracts the practitioner’s attention and is rapidly becoming available through commercial software programs for modeling mineral deposits, oil reservoirs, and groundwater resources. However, when the algorithm only uses hard conditioning data, its inadequacy to model the long-range geological features has always been a research debate in geostatistical contexts. To circumvent this difficulty, one or several pieces of soft information can be introduced into the simulation process to assist in reproducing such large-scale settings. An alternative format of Bayesian sequential indicator simulation is developed in this work that integrates a log-linear pooling approach by using the aggregation of probabilities that are reported by two sources of information, hard and soft data. The novelty of this revisited Bayesian technique is that it allows the incorporation of several influences of hard and soft data in the simulation process by assigning the weights to their probabilities. In this procedure, the conditional probability of soft data can be directly estimated from hard conditioning data and then be employed with its corresponding weight of influence to update the weighted conditional portability that is simulated from the same hard conditioning and previously simulated data in a sequential manner. To test the algorithm, a 2D synthetic case study is presented. The findings showed that the resulting maps obtained from the proposed revisited Bayesian sequential indicator simulation approach outperform other techniques in terms of reproduction of long-range geological features while keeping its consistency with other expected local and global statistical measures.
Open Access
WOMEN IN KAZAKHSTAN’S ENERGY INDUSTRIES: IMPLICATIONS FOR ENERGY TRANSITION
(Energies, 2022) Atakhanova, Zauresh; Howie, Peter
Kazakhstan has a relatively high level of overall gender development, as well as of female employment in its energy industries. Diverse views and backgrounds are necessary to address the challenges of curbing emissions in Kazakhstan, a major fossil fuel producer and exporter. However, our analysis of the Labor Force Survey indicates that female representation among energy sector managers and overall workforce has been falling over time. Moreover, we find that women in Kazakhstan’s coal mining, petroleum extraction, and power industries are concentrated in low-skilled and non-core occupations. Next, by analyzing data on labor compensation within energy occupations, we discover signs of persistent vertical discrimination, which may reduce incentives for women to upgrade their skills. Finally, we find that major shocks, such as the COVID-19 pandemic, may stall or reverse prior progress in increasing the energy sector’s gender diversity. Our findings contribute to raising gender awareness among the stakeholders in Kazakhstan’s energy sector in order to facilitate evidence-based gender mainstreaming.
Open Access
CFD VALIDATION FOR ASSESSING THE REPERCUSSIONS OF FILTER CAKE BREAKERS; EDTA AND SIO2 ON FILTER CAKE RETURN PERMEABILITY
(Applied Artificial Intelligence, 2022) Wayo, Dennis Delali Kwesi; Irawan, Sonny; Khan, Javed; Fitrianti, N. H.
Drill-in-fluids create what are known as filter cakes. Filter cakes, in some cases, lead to well abandonment because they prevent hydrocarbons from flowing freely from the formation into the wellbore. Cake removal is essential to avoid formation damage. A previous study on filter cake breakers was considered for computational fluid dynamic (CFD) validation. Matlab-CFD and Navier-Stokes equations aimed at predicting and validating visual, multiphase flow under finite element analysis (FEA). The interactions of separate chemical breakers and drill-in-fluid such as ethylenediaminetetraacetic acid (EDTA), silica-nanoparticle (SiO2), and biodegradable synthetic-based mud drill-in-fluid (BSBMDIF) were monitored under a particle size distribution, viscosity, density, and pressure. Predicting return permeability of filter cake was considered under a simple filtration process. The particles’ deposition created pore spaces between them; barite 74 μm, nano-silica 150 nm, and EDTA 10 μm generally closed up the pores of the filtration medium. Under extreme drilling conditions, barite formed thicker regions, and EDTA chemical properties easily disjointed these particles, while SiO2 entirely did not. The experimented results of (EDTA) and SiO2 for return permeability were in full force agreeable with the 2D simulation. A hybrid computational analysis considering CFD under discrete element analysis and neural network can be employed for further research validations.
Open Access
ASPHALTENE PRECIPITATION PREDICTION DURING BITUMEN RECOVERY: EXPERIMENTAL APPROACH VERSUS POPULATION BALANCE AND CONNECTIONIST MODELS
(ACS Omega, 2022) Yerkenov, Turar; Tazikeh, Simin; Tatar, Afshin; Shafiei, Ali
Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data preprocessing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery.
Open Access
A COMPARISON BETWEEN THE PERTURBED-CHAIN STATISTICAL ASSOCIATING FLUID THEORY EQUATION OF STATE AND MACHINE LEARNING MODELING APPROACHES IN ASPHALTENE ONSET PRESSURE AND BUBBLE POINT PRESSURE PREDICTION DURING GAS INJECTION
(ACS Omega, 2022) Tazikeh, Simin; Davoudi, Abdollah; Shafiei, Ali; Parsaei, Hossein; Atabaev, Timur Sh.; Ivakhnenko, Oleksandr P.
Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas−oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (R2: 0.793, RMSE: 7.5) and the SVM models (R2: 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements.
Open Access
INVESTIGATION OF BRINE PH EFFECT ON THE RHEOLOGICAL AND VISCOELASTIC PROPERTIES OF HPAM POLYMER FOR AN OPTIMIZED ENHANCED OIL RECOVERY DESIGN
(ACS Omega, 2022) Shakeel, Mariam; Pourafshary, Peyman; Hashmet, Muhammad Rehan
A novel approach to improve viscous and viscoelastic properties by exploiting the pH and salinity sensitivity of HPAM polymer is proposed in this paper. Polymer flooding is a welldeveloped and effective enhanced oil recovery technique. The design of the makeup brine is one of the most critical phases of a polymer flood project, since the brine composition, salinity, and pH directly influence the polymer viscosity and viscoelasticity. However, the viscoelastic properties of hydrolyzed polyacrylamide polymers have not been given much consideration during the design phase of polymer flood projects. Our experimental study focuses on the optimization of the makeup water design for polymer flooding by evaluating the optimum solution salinity and pH for better stability and improved viscoelastic behavior of the polymer. Initially, the brine salinity and ionic composition is adjusted and then hydrolyzed polyacrylamide (HPAM) polymer solutions of varying pH are prepared using the adjusted brine. Rheological experiments are conducted over a temperature range of 25−80 °C and at different aging times. Polymer thermal degradation as a function of pH is assessed by examining the solutions at 80 °C for 1 week. Amplitude sweep and frequency sweep tests are performed to determine the viscoelastic properties such as storage modulus, loss modulus, and relaxation time. A 15−40% increase in the polymer solution viscosity and a 20 times increase in relaxation time is observed in the pH range of 8−10 in comparison to the neutral solution. This can be attributed to the low-salinity ion-adjusted environment of the makeup brine and further hydrolysis and increased repulsion of polymer chains in an alkaline environment. These results indicate that the viscoelastic properties of a polymer are tunable and a basic pH is favorable for better synergy between the brine and the polymer. Alkaline low-salinity polymer solutions have exhibited 60% higher thermal stability in comparison to acidic solutions and thus can be successfully applied in high-temperature reservoirs. The results of this study show that polymer solutions with an optimum pH in the basic range exhibit a higher viscoelastic character and an increased resistance toward thermal degradation. Hence, the polymer solution salinity, ionic composition, and pH should be adjusted to obtain maximum oil recovery by the polymer flooding method. Finally, this study shows that more effective polymer solutions can be prepared by adjusting the pH and designing a low-salinity water/polymer recipe to get the additional benefit of polymer viscoelasticity. The optimized low-salinity alkaline conditions can reduce the residual oil saturation by stronger viscous and viscoelastic forces developed by more viscous polymers. The findings of this study can be employed to design an optimum polymer recipe by tuning the brine pH and salinity for maximum incremental oil recovery, particularly in high-temperature and high-salinity formations.
Open Access
GEOSTATISTICAL MODELING OF HETEROGENEOUS GEO-CLUSTERS IN A COPPER DEPOSIT INTEGRATED WITH MULTINOMIAL LOGISTIC REGRESSION: AN EXERCISE ON RESOURCE ESTIMATION
(Ore Geology Reviews, 2022) Madani, Nasser; Maleki, Mohammad; Soltani-Mohammadi, Saeed
Resource estimation is the main and primary step in the development of a mining project. Principally, it is necessary to first identify the geological domains through boreholes, model them at unsampled locations, and then evaluate the grade(s) of interest inside each built domain. The traditional determination of these categorical domains over the sampling points is suboptimal as it considers mostly-one or two variables from core logging. This leads to the neglect of the influence of other significant variables. To circumvent the problem of estimation domain identification, spatially dependent clustering machine learning algorithms can be of great help in detecting such domains. However, one problem that may appear when using these techniques is that the resulting geo-domains (geo-clusters) obtained by the clustering technique might be heterogeneous and show a non-stationary property. The reason is that the aim of these spatially dependent techniques is to produce compact and spatially contiguous clusters, which are well suited to establishing non-stationary geo-domains. This makes the procedure of modelling challenging as it necessitates the use of advanced geostatistical techniques to propagate the heterogeneous geo-clusters at unsampled locations. An algorithm is presented in this study that employs a non-stationary sequential indicator simulation paradigm to model such complex variability of heterogeneous geo-clusters. Since the spatial trends of underlying geoclusters are required in this simulation method, in this study, we propose the use of multinomial logistic regression to infer these trends. The algorithm was tested using an actual case study from a porphyry copper deposit in Iran, where Cu, Mo, Au, Rock Quality Designation (RQD), mineralization zones, alteration types, and rock types were employed to identify and spatially model the heterogeneous geo-domains in the entire deposit. The results were compared with a conventional sequential indicator simulation where no trend was used. An examination of the resulting maps using several evaluation criteria including visual inspection of the realizations, probability maps, reproduction of proportion of each geo-cluster, connectivity measures, and trend analysis, showed that the findings of the proposed algorithm were superior in modelling heterogeneous geo-domains.
Open Access
MULTISCALE MATRIX-FRACTURE TRANSFER FUNCTIONS FOR NATURALLY FRACTURED RESERVOIRS USING AN ANALYTICAL, INFINITE CONDUCTIVITY, DISCRETE FRACTURE MODEL
(Computational Geosciences, 2021) Hazlett, R. D.; Younis, R.
Fracture matrix transfer functions have long been recognized as tools in modelling naturally fractured reservoirs. If a significant degree of fracturing is present, models involving single matrix blocks and matrix block distributions become relevant. However, this captures only the largest fracture sets and treats the matrix blocks as homogeneous, though possibly anisotropic. Herein, we produce the steady and transient baseline solutions for depletion for such models. Multiscale models pass below grid scale information to the larger scale system with some numerical cost. Instead, for below block scale information, we take the analytic solution to the Diffusivity Equation for transient inflow performance of wells of arbitrary trajectory, originally developed for Neumann boundary conditions, and recast it for Dirichlet boundaries with possible internal fractures of variable density, length, and orientation. As such, it represents the analytical solution for a heterogeneous matrix block surrounded by a constant pressure sink, we take to be the primary fracture system. Instead of using a constant rate internal boundary condition on a fracture surrounded by matrix, we segment the fracture and, through imposed material balance, force the internal complex fracture feature to be a constant pressure element with net zero flux. In doing so, we create a representative matrix block with infinite conductivity subscale fractures that impact the overall drainage into the surrounding fracture system. We vary the internal fracture structure and delineate sensitivity to fracture spacing and extent of fracturing. We generate the complete transient solution, enabling new well test interpretation for such systems in characterization of block size distributions or extent of below block-scale fracturing. The initial model for fully-penetrating fractures can be extended to 3D, generalized floating fractures of arbitrary inclination, and internal complex fracture networks.
Open Access
EVALUATION OF OKRA (ABELMOSCHUS ESCULENTUS) MACROMOLECULAR SOLUTION FOR ENHANCED OIL RECOVERY IN KAZAKHSTAN CARBONATE RESERVOIR
(Energies, 2022-09-18) Abbas, Azza Hashim; Ajunwa, Obinna Markraphael; Mazhit, Birzhan; Martyushev, Dmitriy A.; Bou-Hamdan, Kamel Fahmi; Abd Alsaheb, Ramzi A.
Natural polymers have been investigated as part of the endeavors of green chemistry practice in the oil field. However, natural polymer studies are still preliminary. The current study examines okra’s (natural polymer) efficiency for polymer flooding, particularly in Kazakhstan. The evaluation targets the heavy oil trapped in carbonate reservoirs. SEM and FTIR were used to characterize morphology and chemical composition. A rheology study was conducted under different shear rates for three plausible concentrations: 1 wt.%, 2 wt.% and 5 wt.%. The core flooding was challenged by the low porosity and permeability of the core. The results showed that okra’s size is between 150–900 m. The morphology can be described by rod-like structures with pores and staking as sheet structures. The FTIR confirmed that the solution contains a substantial amount of polysaccharides. During the rheology test, okra showed a proportional relationship between the concentration and viscosity increase, and an inversely proportional relationship with the shear rate. At reservoir temperature, the viscosity reduction was insignificant, which indicated good polymer stability. Okra showed shear-thinning behavior. It was fitted to the Ostwald–de Waele power-law model by a (90–99)% regression coefficient. The findings confirm okra’s pseudo-plasticity, and that it is proportional to the solution concentration. The incremental oil recovery was 7%. The flow was found to be restricted due to the mechanical entrapment resulting from the large molecule size and the low porosity–permeability of the system. This study proves that the dominating feature of natural polysaccharide derivatives is their applicability to moderate reservoir conditions. The current study is a positive attempt at natural polymer application in Kazakhstan and similar field conditions.
Open Access
DATA-DRIVEN ANALYSES OF LOW SALINITY WATERFLOODING IN CARBONATES
(Applied Sciences, 2021-07) Salimova, Rashida; Pourafshary, Peyman; Wang, Lei
Low salinity water (LSW) injection is a promising Enhanced Oil Recovery (EOR) techniquethat has the potential to improve oil recovery and has been studied by many researchers. LSWﬂooding in carbonates has been widely evaluated by coreﬂooding tests in prior studies. A closer lookat the literature on LSW in carbonates indicates a number of gaps and shortcomings. It is difﬁcult tounderstand the exact relationship between different controlling parameters and the LSW effect incarbonates. The active mechanisms involved in oil recovery improvement are still uncertain and moreanalyses are required. To predict LSW performance and study the mechanisms of oil displacement,data collected from available experimental studies on LSW injection in carbonates were analyzedusing data analysis approaches. We used linear regression to study the linear relationships betweensingle parameters and the incremental recovery factor (RF). Correlations between rock, oil, andbrine properties and tertiary RF were weak and negligible. Subsequently, we analyzed the effect ofoil/brine parameters on LSW performance using multivariable linear regression. Relatively stronglinear correlations were found for a combination of oil/brine parameters and RF. We also studied thenonlinear relationships between parameters by applying machine learning (ML) nonlinear models,such as artiﬁcial neural network (ANN), support vector machine (SVM), and decision tree (DT).These models showed better data ﬁtting results compared to linear regression. Among the appliedML models, DT provided the best correlation for oil/brine parameters, as ANN and SVM overﬁttedthe testing data. Finally, different mechanisms involved in the LSW effect were analyzed based on thechanges in the efﬂuent PDIs concentration, interfacial tension, pH, zeta potential, and pressure drop
Open Access
INSIGHTS INTO WETTABILITY ALTERATION DURING LOW-SALINITY WATER FLOODING BY CAPACITANCE-RESISTANCE MODEL
(Petroleum Research, 2022-01-17)
The capacitance-resistance model (CRM) has been widely implemented to model and optimise waterflooding and enhanced oil recovery (EOR) techniques. However, there is a gap in the application of CRM to analyse physical phenomena in porous media as well as the performance of EOR methods, such as low-salinity water (LSW) flooding. The main purposes of this study were to investigate how changes in time constant, as a CRM parameter, can represent physical phenomena in porous media such as wettability alteration. Moreover, to show CRM is a reliable tool to use for interpretation of LSW process as an EOR method. The results of different experimental/modelling studies in this research showed that in CRM model time constant increases when the wettability alters to a water wetness state, whereby the smallest time constant value is observed for the oil wet medium and the highest is observed for the water wet medium. The cases with a gradual alteration in wettability show an increasing trend with the dilution of the injection water. The core flooding data confirms the observed results of the simulation approach. The increment in time constant values indicates the resistance against displacing fluid, which is due to the wettability alteration of the porous medium, resulting in additional oil production. The observations made during this research illustrate that the time constant parameter can be a powerful tool for comparing different EOR techniques, since it is a good indication of the speed of impact of a particular injection fluid on production.
Open Access
DATA-DRIVEN ANALYSES OF LOW SALINITY WATERFLOODING IN CARBONATES
(Applied Sciences, 2021-07) Salimova, Rashida; Pourafshary, Peyman; Wang, Lei
Low salinity water (LSW) injection is a promising Enhanced Oil Recovery (EOR) technique that has the potential to improve oil recovery and has been studied by many researchers. LSW flooding in carbonates has been widely evaluated by coreflooding tests in prior studies. A closer look at the literature on LSW in carbonates indicates a number of gaps and shortcomings. It is difficult to understand the exact relationship between different controlling parameters and the LSW effect in carbonates. The active mechanisms involved in oil recovery improvement are still uncertain and more analyses are required. To predict LSW performance and study the mechanisms of oil displacement, data collected from available experimental studies on LSW injection in carbonates were analyzed using data analysis approaches. We used linear regression to study the linear relationships between single parameters and the incremental recovery factor (RF). Correlations between rock, oil, and brine properties and tertiary RF were weak and negligible. Subsequently, we analyzed the effect of oil/brine parameters on LSW performance using multivariable linear regression. Relatively strong linear correlations were found for a combination of oil/brine parameters and RF. We also studied the nonlinear relationships between parameters by applying machine learning (ML) nonlinear models, such as artificial neural network (ANN), support vector machine (SVM), and decision tree (DT). These models showed better data fitting results compared to linear regression. Among the applied ML models, DT provided the best correlation for oil/brine parameters, as ANN and SVM overfitted the testing data. Finally, different mechanisms involved in the LSW effect were analyzed based on the changes in the effluent PDIs concentration, interfacial tension, pH, zeta potential, and pressure drop.
Open Access
DEVELOPMENT OF A WEIGHTING PROCEDURE FOR GEOMECHANICAL RISK ASSESSMENT
(Energies, 2022-09-06) Mortazavi, Ali; Kuzembayev, Nursultan
Underground mining is one of the riskiest industries. It is well established that the investigation of geomechanical parameters at the design stage of an underground mine provides the approximate rock mass characteristics, which are associated with some risks in the design. From a realistic risk assessment point of view, it is essential to classify risky design parameters as relevant to risk groups and determine a suitable weighting strategy for risk-prone elements aiming at risk assessment. Therefore, a realistic weighting procedure is an essential step in making realistic design decisions to increase the safety of mining operations and economic vitality. This study aimed to develop a realistic weighting procedure to assess and compare various geomechanical parameters that pose a risk to opening stability. In this research, sub-level stoping mining methods, which are commonly used in the Kazakhstan mining industry, were selected to test the developed weighting algorithm. In this study, the risk-prone geomechanical parameters for the chosen mining method were defined, and a weighting procedure was developed using the Fuzzy Analytic Hierarchy Process (FAHP) method. The proposed methodology was verified against available data from the Ridder–Sokolny underground mine, and the analysis results showed good agreement with actual observations in the mine. The obtained preliminary results show that FAHP is a reliable method for weighting geomechanical parameters and can be used as an input in any geomechanical risk assessment practice.
Open Access
APPLICATION OF SOFT COMPUTING TECHNIQUES TO ESTIMATE CUTTER LIFE INDEX USING MECHANICAL PROPERTIES OF ROCKS
(Applied Sciences, 2022) Massalov, Timur; Yagiz, Saffet; Adoko, Amoussou Coffi
The wear of cutting tools is critical for any engineering applications dealing with mechanical rock excavations, as it directly affects the cost and time of project completion as well as the utilization rate of excavators in various rock masses. The cutting tool wear could be expressed in terms of the life of the tool used to excavate rocks in hours or cutter per unit volume of excavated materials. The aim of this study is to estimate disc cutter wear as a function of common mechanical rock properties including uniaxial compressive strength, Brazilian tensile strength, brittleness, and density. To achieve this goal, a database of cutter life was established by analyzing data from 80 tunneling projects. The data were then utilized for evaluating the relationship between rock properties and cutter consumption by means of cutter life index. The analysis was based on artificial intelligence techniques, namely artificial neural networks (ANN) and fuzzy logic (FL). Furthermore, linear and non-linear regression methods were also used to investigate the relationship between these parameters using a statistical software package. Several alternative models are introduced with different input variables for each model, to identify the best model with the highest accuracy. To develop these models, 70% of the dataset was used for training and the rest, for testing. The estimated cutter life by various models was compared with each other to identify the most reliable model. It appears that the ANN and FL techniques are superior to standard linear and non-linear multiple regression analysis, based on the higher correlation coefficient (R2 ) and lower Mean square error (MSE).
Open Access
MULTI-TEMPORAL SAR INTERFEROMETRY FOR VERTICAL DISPLACEMENT MONITORING FROM SPACE OF TENGIZ OIL RESERVOIR USING SENTINEL-1 AND COSMO-SKYMED SATELLITE MISSIONS
(Frontiers in Environmental Science, 2022) Bayramov, Emil; Buchroithner, Manfred; Kada, Martin; Duisenbiyev, Askar; Zhuniskenov, Yermukhan
This study focused on the quantitative assessment of the vertical displacement velocities retrieved using Sentinel-1 and Cosmo-SkyMed synthetic aperture radar images for the Tengiz oilfield. Tengiz oilfield was selected as a study area because of its historically reported continuous subsidence and limited up-to-date studies during recent years. The small baseline subset time-series technique was used for the interferometric processing of radar images acquired for the period of 2018–2020. The geospatial and statistical analyses allowed to determine the existing hotspots of the subsidence processes induced by oil extraction in the study area. Ground deformation measurements derived from the Sentinel 1 and COSMO-SkyMed satellite missions showed that the Tengiz oilfield continuously subsided during 2018–2020 with the maximum annual vertical displacement velocity around −77.4 mm/y and −71.5 mm/y, respectively. The vertical displacement velocities derived from the Sentinel-1 and the COSMO-SkyMed images showed a good statistical relationship with R2≥ 0.73 and RMSE ≤3.68 mm. The cumulative vertical displacement derived from both satellites for the most subsiding location also showed a good statistical relationship with R2 equal to 0.97 and RMSE = ± 4.69. The observed relative differences of measurements by both satellites were acceptable to determine the ongoing vertical surface displacement processes in the study area. These studies demonstrated a practical novelty for the petroleum industry in terms of the comparative assessment of surface displacement measurements using time-series of medium-resolution Sentinel-1 and high-resolution COSMO-SkyMed radar images
Open Access
THE PERFORMANCE OF ENGINEERED WATER FLOODING TO ENHANCE HIGH VISCOUS OIL RECOVERY
(Applied Sciences, 2022) Ganiyeva, Aizada; Karabayanova, Leila; Pourafshary, Peyman; Hashmet, Muhammad Rehan
Low salinity/engineered water injection is an effective enhanced oil recovery method, con firmed by many laboratory investigations. The success of this approach depends on different criteria such as oil, formation brine, injected fluid, and rock properties. The performance of this method in heavy oil formations has not been addressed yet. In this paper, data on heavy oil displacement by low salinity water were collected from the literature and the experiments conducted by our team. In our experiments, core flooding was conducted on an extra heavy oil sample to measure the incremental oil recovery due to the injected brine dilution and ions composition. Our experimental results showed that wettability alteration occurred during the core flooding as the main proposed mechanism of low salinity water. Still, this mechanism is not strong enough to overcome capillary forces in heavy oil reservoirs. Hence, weak microscopic sweep efficiency and high mobility ratio resulted in a small change in residual oil saturation. This point was also observed in other oil displacement tests reported in the literature. By analyzing our experiments and available data, it is concluded that the application of standalone low salinity/engineered water flooding is not effective for heavy oil formations where the oil viscosity is higher than 150 cp and high oil recovery is not expected. Hence, combining this EOR method with thermal approaches is recommended to reduce the oil viscosity and control the mobility ratio and viscous to capillary forces.
Open Access
INTEGRATED ASSESSMENT OF ¬CO2 ECBM POTENTIAL IN JHARIA COALFELD, INDIA
(Scientifc Reports, 2022) Asif, Mohammad; Wang, Lei; Panigrahi, D. C.; Ojha, Keka; Hazlett, Randy
Coalbed methane (CBM) production is efectively achieved by utilizing two processes, viz. primary and secondary recovery. In this paper, the primary recovery of CBM was studied using the adsorption isotherm while CO2-ECBM process for the secondary recovery was simulated with realistic parameters. The adsorption isotherm for CH4 was drawn up to the pressure of 1200 psi for four coal samples and Langmuir isotherm curves for both CH4 and CO2 was measured for one sample up to 2000 psi. The adsorption isotherm of four samples was further utilized for fnding the primary recovery factor of methane, showing that the average primary recovery is~ 54% with the highest recovery factor of ~ 76% for one sample. Hence, CO2-ECBM process could be further implemented to enhance gas recovery. Then, a 3D heterogeneous coalbed model at a depth of 3219 ft was constructed using the COMET3 simulator to demonstrate the potential of CO2-ECBM recovery technique. A concept of break even time was introduced in this study for the comprehension of CO2-ECBM process. It is found that coalbed reservoirs may opt to implement this technology with economically sound recovery
Open Access
CAPILLARY DESATURATION TENDENCY OF HYBRID ENGINEERED WATER BASED CHEMICAL ENHANCED OIL RECOVERY METHODS
(Energies, 2021) Shakeel, Mariam; Samanova, Aida; Pourafshary, Peyman; Hashmet, Muhammad Rehan
Several studies have shown the synergetic benefits of combining various chemical enhanced oil recovery (CEOR) methods with engineered waterflooding (EWF) in both sandstones and carbonate formations. This paper compares the capillary desaturation tendency of various hybrid combinations of engineered water (EW) and CEOR methods with their conventional counterparts. Several coreflood experiments were conducted, including EW-surfactant flooding (EWSF), EW-polymer flooding (EWPF), EW-alkali-surfactant flooding (EWASF), EW-surfactant-polymer flooding (EWSPF), and EW alkali-surfactant-polymer flooding (EWASP). Capillary numbers (Nc) and corresponding residual oil saturation (Sor) for each scenario are compared with capillary desaturation curves (CDC) of conventional CEOR methods from the literature. The results indicate that hybrid EW–CEOR methods have higher capillary desaturation tendency compared to conventional methods. The capillary numbers obtained by standalone polymer flooding (PF) are usually in the range from 10−6 to 10−5 , which are not sufficient to cause a significant reduction in Sor. However, the hybrid EW-polymer flooding approach considerably reduced the Sor for the same Nc values, proving the effectiveness of the investigated method. The hybrid EWASP flooding caused the highest reduction in Sor (23%) against Nc values of 8 × 10−2 , while conventional ASP flooding reduced the Sor for relatively higher Nc values (3 × 10−3 to 8 × 10−1 ). Overall, the hybrid methods are 30–70% more efficient in terms of recovering residual oil, compared to standalone EWF and CEOR methods. This can be attributed to the combination of different mechanisms such as wettability modification by EW, ultralow interfacial tension by alkali and surfactant, reduced surfactant adsorption by alkali addition, and favorable mobility ratio by polymer. Based on the promising results, these hybrid techniques can be effectively implemented to carbonate formations with harsh reservoir conditions such as high salinity and high temperature.
Open Access
COMPARISON OF STRENGTH-BASED ROCK BRITTLENESS INDICES WITH THE BRITTLENESS INDEX MEASURED VIA YAGIZ'S APPROACHES
(Mechanics and Rock Engineering, from Theory to Practice, 2022) Yermukhanbetov, K; Yazitova, A; Yagiz, S
Rock brittleness is one of the most significant properties of rock having a major impact not only on the failure process of intact rock but also on the response of rock mass to rock excavation. In fact, the brittleness is a combination of rock properties including not only the uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) but also density and porosity of rocks. Due to that, the brittleness should be examined very carefully for any excavation projects, i.e., mechanized excavation, drilling and blasting. The aim of this paper is to compare the strength-based brittleness indices with both the rock brittleness index (BIo), directly obtained via punch penetration test (PPT) and also estimated via Yagiz's approach (BI1) as a function of strengths and density of rocks. For the aim, database including more than 45 tunnel cases are used to compute common rock brittleness indices (BI2, BI3, BI4), different combination of UCS and BTS. Further, these indices are compared with both BIo and BI1 as well as each other. It is found that the BIo and BI1 have a significant relations (ranging of determination coefficients (r2 ) from 0.69 to 0.88 with strength-based brittleness indices commonly used in practice. Also, based on findings, several rock brittleness classifications are also revised herein.

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