Synthesis, characterization, and assessment of a CeO2@Nanoclay nanocomposite for enhanced oil recovery

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In this paper, synthesis and characterization of a novel CeO2 /nanoclay nanocomposite (NC) and its effects on IFT reduction and wettability alteration is reported in the literature for the first time. The NC was characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), energy-dispersive X-ray spectroscopy (EDS), and EDS MAP. The surface morphology, crystalline phases, and functional groups of the novel NC were investigated. Nanofluids with different concentrations of 100, 250, 500, 1000, 1500, and 2000 ppm were prepared and used as dispersants in porous media. The stability, pH, conductivity, IFT, and wettability alternation characteristics of the prepared nanofluids were examined to find out the optimum concentration for the selected carbonate and sandstone reservoir rocks. Conductivity and zeta potential measurements showed that a nanofluid with concentration of 500 ppm can reduce the IFT from 35 mN/m to 17 mN/m (48.5% reduction) and alter the contact angle of the tested carbonate and sandstone reservoir rock samples from 139◦ to 53◦ (38% improvement in wettability alteration) and 123◦ to 90◦ (27% improvement in wettability alteration), respectively. A cubic fluorite structure was identified for CeO2 using the standard XRD data. FESEM revealed that the surface morphology of the NC has a layer sheet morphology of CeO2/SiO2 nanocomposite and the particle sizes are approximately 20 to 26 nm. TGA analysis results shows that the novel NC has a high stability at 90◦C which is a typical upper bound temperature in petroleum reservoirs. Zeta potential peaks at concentration of 500 ppm which is a sign of stabilty of the nanofluid. The results of this study can be used in design of optimum yet effective EOR schemes for both carbobate and sandstone petroleum reservoirs

Application of a novel green nano polymer for chemical EOR purposes in sandstone reservoirs: Synergetic effects of different fluid/fluid and rock/fluid interacting mechanisms

In this research, a novel natural-based polymer, the Aloe Vera biopolymer, is used to improve the mobility of the injected water. Unlike most synthetic chemical polymers used for chemical-enhanced oil recovery, the Aloe Vera biopolymer is environmentally friendly, thermally stable in reservoir conditions, and compatible with reservoir rock and fluids. In addition, the efficiency of the Aloe Vera biopolymer was investigated in the presence of a new synthetic nanocomposite composed of KCl-SiO2-xanthan. This chemically enhanced oil recovery method was applied on a sandstone reservoir in Southwest Iran with crude oil with an API gravity of 22°. The Aloe Vera biopolymer’s physicochemical characteristics were initially examined using different analytical instruments. The results showed that the Aloe Vera biopolymer is thermally stable under reservoir conditions. In addition, no precipitation occurred with the formation brine at the salinity of 80,000 ppm. The experimental results showed that adding ethanol with a 10% volume percentage reduced interfacial tension to 15.3 mN/m and contact angle to 108°, which was 52.33 and 55.56% of these values, respectively. On the other hand, adding nanocomposite lowered interfacial tension and contact angle values to 4 mN/m and 48°, corresponding to reducing these values by 87.53 and 71.42%, respectively. The rheology results showed that the solutions prepared by Aloe Vera biopolymer, ethanol, and nanocomposite were Newtonian and fitted to the Herschel-Bulkley model. Finally, core flooding results showed that the application of a solution prepared by Aloe Vera biopolymer, ethanol, and nanocomposite was effective in increasing the oil recovery factor, where the maximum oil recovery factor of 73.35% was achieved, which could be attributed to the IFT reduction, wettability alteration, and mobility improvement mechanisms

A Power Law Committee Scaling Equation for Quantitative Estimation of Asphaltene Precipitation

Precipitation and deposition of asphaltene as a challenging issue have drawn much attention in oil industry owing to severe problems that created during petroleum production and processing. Due to its adverse impact on petroleum production, proposed potent model which capable to predict amount of asphaltene precipitation with high accuracy is necessary. Recently different researcher proposed novel model so-called scaling equation for predicting the amount of asphaltene precipitation. Although derived equation is valuable it possess with flaw as limit accuracy of prediction. The current study proposes a novel technique to predict the accurate value of asphaltene precipitation amount by integration of different scaling equation using the concept of power law committee machine (PLCM). Elements of PLCM model are Rassamdana scaling (RE) model, Hu scaling (HU) model, and Ashoori scaling (AS) model. PLCM model has a parallel architecture that combined the outputs of the aforementioned models in order to reaping the benefits of individual models and increases the accuracy of final asphaltene precipitation amount prediction. Optimal contribution of individual scaling equations in final output is computed by virtue of genetic algorithm (GA) tool. Finally, determined result from PLCM model is compared with individual scaling approaches. It is observed, implementation of PLCM can lead to more accurate prediction compared to different scaling models which conducted alone for predicting amount of asphaltene precipitation

Experimentally Measurements of Relative Permeability in Fractured Core

Naturally Fractured Reservoirs consist of two systems of flow characterization including fracture and matrix systems. The inter-relationship of the fracture and matrix system controls the mechanisms of the oil recovery from naturally fractured reservoirs. Immiscible displacements such as oil and water flooding in naturally fractured reservoirs depend on this inter-flow of matrix-fractured system and fluid properties. Contemporary understanding of multiphase flow through fractures is limited. Numerous studies using synthetic fractures and various fluids lead to different relative permeability curves. In this work fractured core are used to investigate relative permeability of fracture-matrix system. Unsteady state oil-flooding experiments are performed using brine water and kerosene to determine relative permeability curves. This study revealed Relative permeability curves in fractured core cant be consider as straight line and have curvature i.e. non zero capillary pressure in fracture-matrix system. The results also showed fracture orientation can affects oil and water relative permeability curve

Improving Oil Recovery during Water Injection and WAG Processes in Asphaltenic Oil Reservoirs by using Nonionic Surfactants

Asphaltene deposition is one of the most popular problems, which occur during oil production; it can reduce oil recovery through wettability alteration and pore throat blockage. The Aim of this paper is to represent an experimental investigation of reducing oil recovery in water injection and WAG process during asphaltene deposition and improving the oil recovery by using a nonionic surfactant. The results of unsteady state experiments showed that asphaltene deposition reduced the oil recovery in water injection and WAG process significantly, and also lead to rock wettability alteration from water-wet to oil-wet. Also the results of flooding experiments demonstrated that employing the nonionic surfactant improved the recovery through rebounding wettability to water wet

Prediction of Nitrogen Injection Performance in Conventional Reservoirs Using the Correlation Developed by the Incorporation of Experimental Design Techniques and Reservoir Simulation

Enhanced oil recovery using nitrogen injection is a commonly applied method for pressure maintenance in conventional reservoirs. Numerical simulations can be practiced for the prediction of a reservoir performance in the course of injection process; however, a detailed simulation might take up enormous computer processing time. In such cases, a simple statistical model may be a good approach to the preliminary prediction of the process without any application of numerical simulation. In the current work, seven rock/fluid reservoir properties are considered as screening parameters and those parameters having the most considerable effect on the process are determined using the combination of experimental design techniques and reservoir simulations. Therefore, the statistical significance of the main effects and interactions of screening parameters are analyzed utilizing statistical inference approaches. Finally, the influential parameters are employed to create a simple statistical model which allows the preliminary prediction of nitrogen injection in terms of a recovery factor without resorting to numerical simulations

A Novel Combinatorial Approach to Discrete Fracture Network Modeling in Heterogeneous Media

Fractured reservoirs contain about 85 and 90 percent of oil and gas resources respectively in Iran. A comprehensive study and investigation of fractures as the main factor affecting fluid flow or perhaps barrier seems necessary for reservoir development studies. High degrees of heterogeneity and sparseness of data have incapacitated conventional deterministic methods in fracture network modeling. Recently, simulated annealing (SA) has been applied to generate stochastic realizations of spatially correlated fracture networks by assuming that the elastic energy of fractures follows Boltzmann distribution. Although SA honors local variability, the objective function of geometrical fracture modeling is defined for homogeneous conditions. In this study, after the introduction of SA and the derivation of the energy function, a novel technique is presented to adjust the model with highly heterogeneous data for a fractured field from the southwest of Iran. To this end, the regular object-based model is combined with a grid-based technique to cover the heterogeneity of reservoir properties. The original SA algorithm is also modified by being constrained in different directions and weighting the energy function to make it appropriate for heterogeneous conditions. The simulation results of the presented approach are in good agreement with the observed field data

Optimized polymer flooding projects via combination of experimental design and reservoir simulation

The conventional approach for an EOR process is to compare the reservoir properties with those of successful worldwide projects. However, some proper cases may be neglected due to the lack of reliable data. A combination of experimental design and reservoir simulation is an alternative approach. In this work, the fractional factorial design suggests some numerical experiments which their results are analyzed by statistical inference. After determination of the main effects and interactions, the most important parameters of polymer flooding are studied by ANOVA method and Pareto and Tornado charts. Analysis of main effects shows that the oil viscosity, connate water saturation and the horizontal permeability are the 3 deciding factors in oil production. The proposed methodology can help to select the good candidate reservoirs for polymer flooding. Keywords: Polymer flooding, Fractional factorial design, Reservoir simulation, P-value, ANOV

Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in a Heterogeneous Carbonate Reservoir

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this research, SVM classification method is used for lithology prediction from petrophysical well logs based on petrographic studies of core lithology in a heterogeneous carbonate reservoir in southwestern Iran. Data preparation including normalization and attribute selection was performed on the data. Well by well data separation technique was used for data partitioning so that the instances of each well were predicted against training the SVM with the other wells. The effect of different kernel functions on the SVM performance was deliberated. The results showed that the SVM performance in the lithology prediction of wells by applying well by well data partitioning technique is good, and that in two data separation cases, radial basis function (RBF) kernel gives a higher lithology misclassification rate compared with polynomial and normalized polynomial kernels. Moreover, the lithology misclassification rate associated with RBF kernel increases with an increasing training set size