Modeling Asphaltene Precipitation Under Gas Injection and Pressure Depletion Conditions

Asphaltene precipitation is a major problem during primary oil production and enhanced oil recovery in the petroleum industry. In this work, a series of experiments was carried to determine the asphaltene precipitation of bottom hole live oil during gas injection and pressure depletion condition with Iranian bottom hole live oil sample, which is close to reservoir conditions using high pressure-high temperature equilibrium cell. In the majority of previous works, the mixture of recombined oil (mixture dead oil and associated gas) was used which is far from reservoir conditions. The used pressure ranges in this work covers wide ranges from 3 to 35 MPa for natural depletion processes and 24–45 MPa for gas injection processes. Also, a new approach based on the artificial neural network (ANN) method has been developed to account the asphaltene precipitation under pressure depletion/gas injection conditions and the proposed model was verified using experimental data reported in the literature and in this work. A three-layer feed-forward ANN by using the Levenberg-Marquardt back-propagation optimization algorithm for network training has been used in proposed artificial neural network model. The maximum mean square error of 0.001191 has been found. In order to compare the performance of the proposed model based on artificial neural network method, the asphaltene precipitation experimental data under pressure depletion/gas injection conditions were correlated using Solid and Flory-Huggins models. The results show that the proposed model based on artificial neural network method predicts more accurately the asphaltene precipitation experimental data in comparison to other models with deviation of less than 5%. Also, the number of parameters required for the ANN model is less than the studied thermodynamic models. It should be noted that the Flory and solid models can correlate accurately the asphaltene precipitation during methane injection in comparison with CO₂ injection.     

Neural computations in modelling of CO2 capture from Gas stream emissions by Sodium Glycinate solution

The present contribution was performed in order to predict CO₂ loading capacity in aqueous sodium glycinate as a novel class of green solution under wide operating range using radial basis function artificial neural network (RBFANN). The predicted CO₂ loading capacity values were in brilliant agreement with those corresponding experimental values. The estimated values of MSE and R-squared were 0.00045 and 0.997, respectively. Accordingly, statistical and graphical analyses confirm satisfactory prediction of our proposed model.     

Phase equilibrium modeling of semi-clathrate hydrates of the CO2+H2/CH4/N2+TBAB aqueous solution system

In this study, the semi-clathrate hydrate dissociation pressure for the CO₂+N₂, CO₂+H₂, CO₂+CH₄, and pure CO₂ systems in the presence of different concentrations of TBAB aqueous solutions is predicted using a strong machine learning technique of multi-layer perceptron neural network (MLP-NN). The developed model, with an overall correlation coefficient (R²) of 0.9961 and mean square error (MSE) of 5.96E?02, presented an excellent accuracy in prognosticating experimental data. A complete statistical evaluation performed to promise the strength and generality of the multi-layer perceptron artificial neural network (MLP-ANN). In addition, the applicability of the proposed network and quality of experimental data was assessed through the Leverage approach.     

Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations

Development of robust predictive models to estimate the transport properties of gases (namely viscosity and thermal conductivity) is of immense help in many engineering applications. This study highlights the application of the artificial neural network (ANN) and least squares support vector machine (LSSVM) modeling approaches to estimate the viscosity and thermal conductivity of CO₂. To propose the machine learning methods, a total of 800 data gathered from the literature covering a wide temperature range of 200–1000 K and a wide pressure range of 0.1–100 MPa were used. Particle swarm optimization (PSO) and genetic algorithm (GA) as population-based stochastic search algorithms were applied for training of ANNs and to achieve the optimum LSSVM model variables. For the purpose of predicting viscosity, the PSO-ANN and GA-LSSVM methods yielded the mean absolute error (MAE) and coefficient of determination (R²) values of 1.736 and 0.995 as well as 0.51930 and 0.99934, respectively for the whole data set, while for the purpose of predicting thermal conductivity, the PSO-ANN and GA-LSSVM models yielded the MAE and R² values of 1.43044 and 0.99704 as well as 0.72140 and 0.99857, respectively for the whole data set. Both methods provide properly capable method for predicting the thermal conductivity and viscosity of CO₂.     

The Feasibility of Lab-scale Cyclic CO2 Injection in Fractured Porous Media

A comprehensive analysis of the CO₂ Huff-n-Puff process with application to representative light oil system contained within a fractured porous media is present in this article. To accomplish this work, a simulation model representative of the laboratory experimental model was built and constrained under similar laboratory conditions. Six sets of CO₂ Huff-n-Puff experiments were conducted at injection pressures of 1723–10342 kPa; moreover, additional sensitivity analysis was performed on the 5170 and 6894 kPa conditions for different permeability and porosity. Results of this study demonstrate that cyclic injection of CO₂ under miscible conditions performs more favorably than under immiscible injection conditions.     

Carbon dioxide injection for enhanced gas recovery and storage (reservoir simulation)

CO₂ injection for enhanced oil recovery (EOR) had been broadly investigated both physically and economically. The concept for enhanced gas recovery (EGR) is a new area under discussion that had not been studied as comprehensively as EOR. In this paper, the “Tempest” simulation software was used to create a three-dimensional reservoir model. The simulation studies were investigated under different case scenarios by using experimental data produced by Clean Gas Technology Australia (CGTA). The main purpose of this study is to illustrate the potential of enhanced natural gas recovery and CO₂ storage by re-injecting CO₂ production from the natural gas reservoir. The simulation results outlined what factors are favourable for the CO₂-EGR and storage as a function of CO₂ breakthrough in terms of optimal timing of CO₂ injection and different injection rates. After analysing the results for each case scenario, it had been concluded that CO₂ injection can be applied to increase natural gas recovery simultaneously sequestering a large amount of the injected CO₂ for this particular gas reservoir. In addition, various CO₂ costs involved in the CO₂-EGR and storage were investigated to determine whether this technique is feasible in terms of the CO₂ content in the production as a preparation stage to achieve the economic analysis for the model.     

Modeling CO2 miscible flooding for enhanced oil recovery

The injection of fuel-generated CO2 into oil reservoirs will lead to two benefits in both enhanced oil recovery (EOR) and the reduction in atmospheric emission of CO2. To get an insight into CO2 miscible flooding performance in oil reservoirs, a multi-compositional non-isothermal CO2 miscible flooding mathematical model is developed. The convection and diffusion of CO2-hydrocarbon mixtures in multiphase fluids in reservoirs, mass transfer between CO2 and crude, and formation damages caused by asphaltene precipitation are fully considered in the model. The governing equations are discretized in space using the integral finite difference method. The Newton-Raphson iterative technique was used to solve the nonlinear equation systems of mass and energy conservation. A numerical simulator, in which regular grids and irregular grids are optional, was developed for predicting CO2 miscible flooding processes. Two examples of one-dimensional (1D) regular and three-dimensional (3D) rectangle and polygonal grids are designed to demonstrate the functions of the simulator. Experimental data validate the developed simulator by comparison with 1D simulation results. The applications of the simulator indicate that it is feasible for predicting CO2 flooding in oil reservoirs for EOR.     

Hybrid Neural Network Performance Prediction Model for Gas Assisted Gravity Drainage Recovery Method Based on the Scaling Analysis

Hybrid system is a potential tool to deal with nonlinear regression problems. The authors present an efficient prediction model for gas assisted gravity drainage injection recovery process based on artificial neural network (ANN) and dimensionless groups. Ant colony optimization (ACO) is applied to determine the network parameters. Results show that ACO optimization algorithm can obtain the optimal parameters of the ANN model with very high predictive accuracy. The predicted recovery from the ACO-ANN model, in comparison with other proposed models in literature, were in good agreement with those measured from simulations, and were comparable to those estimated from the other proposed models.     

A mathematical model for predicting growth/no-growth of psychrotrophic C. botulinum in meat products with five variables

The objective of this study was to develop a mathematical model for predicting growth/no-growth of psychrotrophic Clostridium botulinum in pasteurised meat products packed in modified atmosphere for combinations of storage temperature, pH, NaCl, added sodium nitrite and sodium lactate.Data for developing and training the artificial neural network (ANN) were generated in meat products. A total of 249 growth experiments were carried out in three different meat products with different combinations of storage temperature, pH, NaCl, sodium nitrite and sodium lactate. The meat batter was inoculated with approx. 10⁴ spores/g using a 4-strain cocktail of gas-producing C. botulinum. The meat products were sliced, packed in modified atmosphere (30% CO₂/70% N₂) and stored at 4 °C, 8 °C and 12 °C, respectively, for up to 8 weeks. The enumeration of C. botulinum was performed when the volume of the package had increased by 9% or more, or at the end of the storage period.Based on 10–20 replicates for each combination, the “frequency of growth” was calculated. An ANN with 5 input neurons, 3 hidden and a single output neuron was trained using the 5 hurdle values as inputs and the observed “frequency of growth” as target value.The inputs for the final model are the five variables: temperature, pH, added sodium nitrite, NaCl and sodium lactate within the ranges 4–12 °C, 5.4–6.4, 0–150 ppm, 1.2–2.4% and 0–3% respectively. As reference a logistic regression method was also applied and subsequently compared to the full neural network model. Based on RMSEC value of 0.104 and 0.144 for ANN and the logistic regression model respectively, the ANN was preferred. On a separate set of test data (n = 60) the ANN model was validated by comparing the predicted “probability of growth” with the observed growth. A bias of 0.0166 was obtained, indicative of a model that is slightly fail-safe.     

Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery

Development of reliable and accurate models to estimate carbon dioxide–brine interfacial tension (IFT) is necessary, since its experimental measurement is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure. In the current study, feed forward artificial neural network is used for estimation of CO₂–brine IFT based on data from published literature which consists of a number of carbon dioxide–brine interfacial tension data covering broad ranges of temperature, total salinity, mole fractions of impure components and pressure. Trial-and-error method is utilized to optimize the artificial neural network topology in order to enhance its capability of generalization. The results showed that there is good agreement between experimental values and modeling results. Comparison of the empirical correlations with the proposed model suggests that the current model can predict the CO₂–brine IFT more accurately and robustly.     

A quick evaluation model for CO<Subscript>2</Subscript> flooding and sequestration

CO₂ flooding not only triggers an increase in oil production, but also reduces the amount of CO₂ released to the atmosphere (by storing it permanently in the formations). It is one of the best ways to use and store CO₂. This paper firstly selects the key factors after analyzing the factors influencing the CO₂ storage potential in the formations and oil recovery, and then introduces a series of dimensionless variables to describe reservoir characteristics. All influencing factors with varying values are calculated through a Box-Behnken experimental design. The results are interpreted by a response surface method, and then a quick screening model is obtained to evaluate the oil recovery and CO₂ storage potential for an oil reservoir. Based on the evaluation model, sensitivity analysis of each factor is carried out. Finally, research on CO₂ sequestration and flooding in a typical reservoir indicates that the evaluation model fits well with the numerical simulation, which proves that the evaluation model can provide criteria for screening attractive candidate reservoirs for CO₂ sequestration and flooding.     

The Evaluation of CO2 Miscible Displacement and Storage Effect in Mature Oil Fields

CO₂ displacement and storage technology is a feasible technology for developing countries to relieve energy supply and control climate change. Based on the fractional flow theory, considering comprehensive key factors affect CO₂ flow in reservoir, including viscous fingering, gravity separation, and reservoir heterogeneity, to establish CO₂ miscible displacement mathematical model and develop evaluation software CS-EOR. Aiming at the concrete reservoir, the oil displacement and CO₂ storage efficiency assessment were carried out, and the CO₂ utilization coefficients were quantified. The results will provide important theoretical foundation and reliable tool for further the evaluation of CO₂ injection project in mature oil reservoir around the word.     

Prediction of the interfacial tension between hydrocarbons and carbon dioxide

In the recent years due to increasing demand for energy and declination of reservoir production, an impressive notice on enhancement of oil recovery has been found. The gas injection especially carbon dioxide injection due to low cost and friendly environmentally of this approach the special attention to CO₂ injection increased. The miscibility is known as key factor which effects on enhancement of recovery. The miscibility is controlled by interfacial tension of hydrocarbons and carbon dioxide so the importance of investigation of the interfacial tension becomes highlighted.in this investigation by using radial basis function (RBF) artificial neural network (ANN) as a novel approach the interfacial tension of hydrocarbons and carbon dioxide in terms of pressure, temperature, liquid and gas densities and molecular weight of alkane. The graphical and statistical results illustrated the fact that RBF-ANN algorithm is applicable for estimation of interfacial tension between hydrocarbons and carbon dioxide with great accuracy.     

Black Oil and Compositional Reservoir Simulation for Increasing the Recovery Performance of an Iranian Fractured Carbonate Reservoir

Abstract

Naturally fractured reservoirs contain a significant amount of world oil reserves. Accurate and efficient reservoir simulation of naturally fractured reservoirs is one of the most important, challenging, and computationally intensive problems in reservoir engineering. Black oil and compositional reservoir simulators have been used to determine the reservoir management and production strategies to increase the oil recovery from a low-porosity, low-permeability fractured carbonate reservoir, with an average matrix permeability of 0.8 md, average fracture permeability of 500 md, and an average matrix porosity of 10%. This reservoir is a candidate for an enhanced oil recovery (EOR) process, because the reservoir production rate has been declined due to increasing the water cut as a result of rising the water oil contact. The injection techniques that have been considered in this study for black oil model include (a) gas injection, (b) water injection, and (c) simultaneous water alternating gas injection and for the compositional model include (a) dry gas injection, (b) CO₂ injection, and (c) N₂ injection. Simulation results show that CO₂ injection has the maximum oil recovery between the EOR scenarios.     

The Effects of Miscible CO2 Injection on Oil Biomarker Parameters

The purpose of this research was to study the effect of CO₂ injection on the geochemistry of crude oil in order to determine the probability of using geochemical parameters for monitoring CO₂ injection. In this process, four oil samples from different offshore oil fields were collected, synthetic steady state oil reservoir (porous media) were made by slim tube apparatus, then CO₂ injection process was done in different pressures. Various geochemical analyses were also carried out on the injected oil before and after the injection. The results show that the bulky changes on oil sample by CO₂ injection. CO₂ injection is more likely to precipitate complex and large molecules such as asphaltenes-resins and also large normal alkanes. In this case, the percentage of aromatic molecules was increased during injection. In general view on chromatograms, the height and abundance of all saturated compound peaks after CO₂ injection were significantly decreased. However, biomarker analysis shows that CO₂ injection has a tendency to change source and maturity biomarker parameters.     

CO2 sequestration through enhanced oil recovery in a mature oil field

Recent advances in enhanced oil recovery (EOR) technology create new opportunities for CO₂ sequestration. This paper proposes a technical–economic model for underground storage of CO₂ emitted by a fertilizer industry in the Northeast of Brazil, in a hypothetical mature oil reservoir through EOR operation. Simulations based on mass, energy and entropy balances, as well as economic analysis, were assessed for the process of CO₂ sequestration combined with EOR. This model takes into account the energy requirements for the whole CO₂ sequestration process, as well as the emissions inherent to the process. Additionally, a breakdown cost methodology is proposed to estimate the main financial determinants of the integrated EOR with CO₂ sequestration (costs of CO₂ purchase, compression, transportation and storage). Project evaluation is derived from a cash flow model, regarding reservoir production profile, price and costs, capital expenditures (CAPEX), operating expenditures (OPEX), carbon credits, depreciation time, fiscal assumptions etc. A sensitivity analysis study is carried out to identify the most critical variables. Project feasibility, as expected, is found to be very sensitive to oil price, oil production, and CAPEX. Moreover, there is the contribution from the mitigation of the greenhouse gas (GHG) by storing a significant amount of CO₂ in the reservoir where it can remain for thousands of years.     

The Use of ANN and the Mathematical Model for Prediction of the Main Product Yields in the Thermal Cracking of Naphtha

Abstract

Thermal cracking of naphtha has such numerous reaction routes that the detailed reaction mechanism has not yet been determined. In this regard, a model of artificial neural networks (ANNs), using back propagation (BP), is developed for modeling thermal cracking of naphtha. The optimum structure of the neural network was determined by a trial-and-error method. Different structures were tried with several neurons in the hidden layer. The model investigates the influence of the coil outlet temperature, the pressure of the reactor, the steam ratio (H2O/naphtha), and the residence time on the pyrolysis product yields. A good agreement was found between model results and experimental data. A comparison between the results of the mathematical model and the designed ANN was also conducted and the relative absolute error was calculated. Performance of the ANN model was better than the mathematical model.     

Scaled 3-D model studies of immiscible CO2 flooding using horizontal wells

In this study, a comprehensive laboratory investigation was conducted for the recovery of heavy oil from a scaled three-dimensional (3-D) physical model, packed with 18° API gravity crude oil, brine and crushed limestone. A total of 20 experiments were conducted using the scaled 3-D physical model with 30×30×6 cm³ dimensions. Basically, four different immiscible CO₂–water displacement processes were used for recovering heavy oil: (i) continuous CO₂ injection, (ii) waterflooding, (iii) simultaneous injection of CO₂ and water, and (iv) water alternating gas (WAG) process. Three groups of well configurations were mainly used: (1) vertical injection and vertical production wells, (2) vertical injection and horizontal production wells, and (3) horizontal injection and horizontal production wells. Base experiments were run with water only and carbon dioxide alone and optimum rates for WAG and simultaneous water–CO₂ injection were determined. In continuous CO₂ injection, highest recovery was obtained by vertical injection–horizontal production (VI–HP), followed by vertical injection–vertical production (VI–VP) and the least by horizontal injection–horizontal production (HI–HP). In VI–HP configuration, the best recovery was obtained as 15.1% OOIP. Higher oil recovery was obtained with a VI–HP wells than with a pair of vertical wells and horizontal wells. The WAG 1:5 ratio yielded a final recovery of 34.5% OOIP with VI–VP well configuration and 17.0% OOIP of additional recovery over waterflooding. In turn, the WAG 1:10 ratio was the best with a final recovery of 20.9% of OOIP with VI–HP well configuration. Oil production from WAG injection is higher than that obtained from the injection of continuous CO₂ or waterflooding alone.     

Understanding aqueous foam with novel CO2-soluble surfactants for controlling CO2 vertical sweep in sandstone reservoirs

The ability of a novel nonionic CO₂-soluble surfactant to propagate foam in porous media was compared with that of a conventional anionic surfactant (aqueous soluble only) through core floods with Berea sandstone cores. Both simultaneous and alternating injections have been tested. The novel foam outperforms the conventional one with respect to faster foam propagation and higher desaturation rate. Furthermore, the novel injection strategy, CO₂ continuous injection with dissolved CO₂-soluble surfactant, has been tested in the laboratory. Strong foam presented without delay. It is the first time the measured surfactant properties have been used to model foam transport on a field scale to extend our findings with the presence of gravity segregation. Different injection strategies have been tested under both constant rate and pressure constraints. It was showed that novel foam outperforms the conventional one in every scenario with much higher sweep efficiency and injectivity as well as more even pressure redistribution. Also, for this novel foam, it is not necessary that constant pressure injection is better, which has been concluded in previous literature for conventional foam. Furthermore, the novel injection strategy, CO₂ continuous injection with dissolved CO₂-soluble surfactant, gave the best performance, which could lower the injection and water treatment cost.     

延长油田致密砂岩油藏CO2驱油机理研究

CO_2驱是提高低渗透油田产量、缓解温室效应的有效途径。针对鄂尔多斯盆地油藏压力系数低、原油轻质组分含量高的特点,通过PVT和最小混相压力等测试分析方法,揭示了低压、低孔、低渗油藏CO_2驱提高采收率主要机理。开展了CO_2注入储层与无机、有机物作用后的沉淀研究,表明CO_2在无机盐溶液中不会形成沉淀堵塞孔隙,CO_2与有机质作用后沉积点高于油藏压力,且注入压力越高,CO_2在地层原油中的溶解能力越强,目标区块CO_2注入后不易形成沥青质沉淀。物模驱替实验结果表明,均质岩心的采出程度明显高于非均质岩心,且随着岩心非均质性的增加,水驱采出程度、气驱采出程度及最终采出程度均明显下降。