Publisher: Research Institute of Petroleum Industry   (Total: 1 journals)   [Sort by number of followers]

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J. of Petroleum Science and Technology     Open Access  
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Journal of Petroleum Science and Technology
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2251-659X - ISSN (Online) 2645-3312
Published by Research Institute of Petroleum Industry Homepage  [1 journal]
  • Simulation of the effect of Asphaltene precipitation on wells’
           productivity Index

    • Abstract: Asphaltene precipitation could decrease the effective mobility of hydrocarbon through pore throats plugging and as a result, the rock permeability. Moreover, by changing reservoir rock wettability, asphaltene diminishes the effective permeability of oil and increases the amount of residual oil. In this regard, changing fluid viscosity could also be a determining factor too.This study is aimed at modeling asphaltene precipitation and evaluating this phenomenon in terms of its effects on wells’ Productivity Index (PI). In this study, after implementing a thermodynamic asphaltene model based on Cubic Peng-Robinson EOS and taking advantage of the solid model as the asphaltene model, the fluid model was exported into a commercial compositional simulator using an in-house PVT software. Then, dynamics of the precipitated asphaltene were investigated using deposition, porosity reduction, permeability reduction, and viscosity change models in a commercial compositional simulator. Additionally, a sensitivity analysis was done to see how deposition model parameters and also the production rate, can probably affect the formation damage and final Wells’ PI under the reservoir natural depletion. Finally, the most significant parameters triggering asphaltene damage were identified. The results indicated that the damage triggered by asphaltene deposition, through plugging and decreasing porosity and permeability, and also a change in viscosity was disclosed as a decline in wells’ PI. The results also showed that although adsorption had a significant effect on the decline in wells’ PI, but plugging is a more determining mechanism.
  • Introducing a MATLAB Code as a Statistical Approach for Fracture Networks

    • Abstract: Fracture network modeling is important in simulating fluid flow, identifying reservoirs storage areas, recognizing aquifers, and managing the groundwater pathway to prevent wall failure in mine stability consideration. In other words, precise estimation of mass transportation and hydrology parameters depends on the accuracy of fracture modeling. In this study, a new iterative fracture network-modeling MATLAB code is presented which directly models the statistical geometry of the fractures. The code is employed to simulate parameters of fractures in terms of density, orientation as well as length distribution. To demonstrate the effectiveness of the presented code, the method is applied on a real 2-Dimensional fracture network image from an exposed wall and its performance is assessed by three different criteria including fracture length distribution, producing fracture orientation, and fracture density. According to the assessment results, the statistical method is capable of reproducing the length distribution and density of the fracture network similar to the reference. In addition, the method performs almost well in modeling the orientation of fractures.
  • Development of an artificial neural network model to calculate static
           Young’s modulus based on a log-derived data base

    • Abstract: Static Young's Modulus is a measure of reservoir rock stiffness and best determined by experimental studies on cores. However, the experimental procedures are demanding with considerable cost. On the other hand, a dynamic Young's modulus can easily be estimated from readily available petrophysical data. The static young's modulus can then easily be obtained from dynamic counterpart by empirical relationships. This research is an attempt to use AI techniques to predict Static Young's modulus. 2350 data sets were collected from number of wells located in the Middle East with sandstone and limestone lithology and used to build an AI model. Each data set contains static Young’s Modulus as a function of the bulk density, shear wave arrival time and compressional wave arrival time. An artificial neural network (ANN) model was developed to predict the static young’s modulus with high accuracy of R2 = 0.999 and AARE of 0.028%. The proposed model was validated with measured reservoir rock data and was compared with four different correlations and the results showed that the model provided the highest coefficient of determination (R2) and the lowest standard deviation.
  • Performance of water injection and CO2 injection into oil reservoirs based
           on field data: using ANNs to predict in the selected scenario

    • Abstract: CO2-EOR, not only provides economic returns from produced oil , but it also has environmental benefits. In this study, an oil reservoir is simulated using field data to compare this method with the water injection method and natural depletion method of the reservoir. Compared to natural depletion, water injection and CO2 injection increased oil recovery by 8.4% and 12.7%, respectively. By choosing the scenario of CO2 injection to reduce the computational load and also the possibility of using it in optimization tasks, the surrogate reservoir model was built using machine learning (ML) technique. Therefore, using the data-driven model, we are able to reproduce the data related to the CO2-EOR process in a much shorter period of time, thereby allowing us to select the most efficient parameters and their ranges for different process. The ANN is applied to the data and trained after the database is built and the hyper-parameters have been optimized. The trained two-objective ANN was a MAPE of less than 2.5% in the test data for both objectives, i.e., oil recovery and carbon dioxide storage. To further validation and ensure the accuracy of trained ANN, the numerical simulator was run randomly 10 times and compared with the values predicted by the ANN. MAPE for both objectives was less than 2.6%. Therefore, the ANN that makes predictions in a fraction of a second has a suitable accuracy that can be used as a surrogate reservoir model.
  • Enhancement of efficiency of water removal from Bangestan crude oil by
           silica nanoparticles using imidazolium-based ionic liquids

    • Abstract: The effect of coating silica nanoparticles by a number of 1-alkyl-3-methylimidazolium hexafluorophosphate ionic liquids of [Rmim][PF6] general formula (R= C10, C12, and C14) on the water removal efficiency of silica nanoparticles from crude oil emulsions has been studied in this work. The ionic liquids have been prepared and characterized by a comparison of their 1HNMR and FT-IR spectral data with those reported in the literature. The effects of factors including cation alkyl chain length and concentration of the ionic liquids prepared on the water separation efficiency of the demulsifier have also been investigated in order to determine the optimal values for the chain length and concentration of the ionic liquid. [C14mim][PF6] ionic liquid at a concentration of 1200 ppm was shown to be the most efficient ionic liquid among the ionic liquids studied. The water separation efficiency achieved using coated nanosilica under the optimal conditions determined was found to be 93.3%.
  • The effect of porosity on the seismic waves velocities and elastic
           coefficients in a South-Western Iran's oil field

    • Abstract: Petrophysical and geophysical laboratory measurements were performed on ⁽40⁾ samples made of sandstone and limestone in an Oil Field. Parameters including porosity, density and permeability were measured along with the of compressional and shear waves’ velocities of the samples under reservoir conditions. Also, the study of microscopic thin sections, factors affecting the velocity of waves including porosity, Poisson’s ratio, density, pressure and pore type were investigated. The scattering of points in the velocity diagrams of elastic waves based on the petrophysical parameters of the rock indicates that the most important factor of velocity changes is the pore type in the samples in the same porosity value. So, ‹LMR› parameters were calculated using laboratory results of velocity measurement. The values of ‹LMR› parameters of seismic data were determined by the velocities of compressional and shear waves in the pre-stack stage seismic. Then, using seismic inversion, compressional and shear wave resistances were estimated and seismic sections with ‹λ.μ› and ‹μ.ρ› parameters were created. The results show that there is a good correlation between laboratory measurement of rock physics and pre-stack seismic data. Also, the factor affecting the velocity of waves, i.e., the pore types, should also be considered. Uncertainty in velocity values due to the diversity of pores can show differences in the velocity of elastic waves in the same porosity value to about ⁽1500⁾ m/s. Also, at a constant velocity, porosity changes of up to ⁽20⁾ percent are visible.
  • Comparison of cementation factor determination by artificial neural
           network method and optimized experimental relations in carbonate rocks

    • Abstract: The cementation factor is one of the basic parameters for the calculating water saturation and then hydrocarbon saturation of reservoirs. The best way to determine the cementation factor is through laboratory measurements. To generalize this coefficient for samples without laboratory measurements, experimental relationships versus petrophysical properties by researchers can be somewhat helpful. The method of artificial neural networks, with the help of training, validation, and data analysis, has given the can better results in determining the cementation factor of carbonate samples. It is one of the best method that can use the petrophysical data as training data and make acceptable predictions with analytical methods. Therefore, laboratory measurement of the cementation factor has been performed for 159 carbonate cores from the Sarvak formation in the southwest of Iran. For the studied samples, the cementation factor in porosity was determined as a quadratic equation with the highest correlation coefficient. In this study, the compatibility of experimental relationship shows better conformity by considering the permeability of each sample. Improvement of empirical relationships by the authors, Correlation coefficients between the laboratory data and the experimental relationships has been increased. It is better to use improved experimental relationships for the studied carbonate samples.To process the data, the best adapt the laboratory data, and present a suitable model, artificial neural network methods, have been used. The Bayesian Regularization algorithm with five hidden layers has the least error in the test, validation, and testing stages
  • Laboratory Study of the Application of a Novel Bio-Based Polymer to
           Synthesize Aphron Drilling Fluids

    • Abstract: Aphron fluids are a special type of foam in which the gas bubble (air or any other gas) is surrounded by a double-walled layer consisting of surfactant and polymer. Therefore, it is more stable at high temperature and high pressure conditions due to being preserved by three layers. These fluids are widely used in industry. Today, the industry uses various types of polymers such as acrylamides, polyacrylamides and hydrolyzed acrylamides that are harmful to the environment.In this study, the possibility of using natural and biodegradable polymers such as Astragalus Gum and starch is investigated. The results showed that the Aphron fluid made from Astragalus Gum (extracted from a dissert plant) had higher volumetric yield than industrial polymers. Stability over time as well as rheological properties for Astragalus Gum is also acceptable. Also, it is found that increasing the polymer concentration, the stability and rheological properties increase, but in contrast, the volumetric yield decreases. The effect of salt and surfactant concentrations are also evaluated in this research. The results show that increasing the surfactant concentration increases the volumetric yield of the fluid. Increasing the surfactant concentration from 1 wt% to 2 wt% increases the volumetric efficiency of Aphron fluids by 5%. In general, according to the results obtained in this research work, it can be inferred that the performance of Astragalus Gum is better than industrial polymer for making Aphron fluids.
  • Erosion Damage for Various Flow Regimes during Particle Transport in Oil
           Wells: CFD Study

    • Abstract: Oil extraction from weak sandstone formations that fails under changing in situ stresses leads to fine migration in near wellbore region. Companies use selective completion practices or downhole filters to control particle production in oil wells . However there will always remain fines with various size dispersions that cause erosion damage in crude oil pipelines. Particles influence oil viscosity as well as oil density therefore impact flow regime rather than pressure drop in various depths. In this work we employ Fluent software to simulate particle transport with multiphase flow in annulus that models cutting extraction during drilling rather than pipes which simulates production process. By adjusting various dissimilar particle dispersion functions we step farther to estimate damage due to erosive flow under various flow regimes. Results show that since thin film of liquid slurry with high particle concentration forms near inner wall pipe in annular flow regime erosion damage is at its highest value. Outcomes also illustrate that at high drill pipe rotation rates flow conflicts therefore erosion damage in annulus increases significantly.
  • Performance Analysis of Enhanced Gas Recovery Approach

    • Abstract: Most massive gas fields in Bangladesh are nearing the end of their production. As global energy demands rise due to the rising population and rapid urbanization, maximizing the use of available resources has become essential. The preparation of the field's measurement to extend field lifetime needs to get attention. One such measure is Enhanced Gas Recovery (EGR), a potential technique to maximize the efficiency of the recovery process, which utilizes fracturing, water flooding, and gas injections to increase gas production. This work presents a simulation study of the performance of three EGR techniques with linear, triangular, and corner injection well placements and analyzes the simulation results of the techniques. Simulation of water flooding, CO2 injection, and WAG (water alternating CO2 gas) techniques are performed to evaluate the performance of the reservoir under these injections, and a suggestion has been provided in favor of the suitable approach among them. The performances are evaluated based on two factors: how much additional gas has been recovered and the quality of the produced gas. After analyzing the results for each case scenario, it is concluded that CO2 injection can be applied to increase natural gas recovery up to 24.55% more than the base case model, while the water flooding model and WAG model contributed 16.57% and 8% more gas recovery, respectively. The EGR techniques are simulated using the GASWAT feature in the fully implicit formulation of the E300 compositional simulator, a tool of the ECLIPSE suite.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762

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