Gas Analysis by In Situ Combustion in Heavy‐Oil Recovery Process: Experimental and Modeling Studies

Enormous efforts have been made to facilitate produced‐gas analyses by in situ combustion implication in heavy‐oil recovery processes. Robust intelligence‐based approaches such as artificial neural network (ANN) and hybrid methods were accomplished to monitor CO₂/O₂/CO. Implemented optimization approaches like particle swarm optimization (PSO) and hybrid approach focused on pinpointing accurate interconnection weights through the proposed ANN model. Solutions acquired from the developed approaches were compared with the pertinent experimental in situ combustion data samples. Implication of hybrid genetic algorithm and PSO in gas analysis estimation can lead to more reliable in situ combustion quality predictions, simulation design, and further plans of heavy‐oil recovery methods.     

Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling

Water coning in petroleum reservoirs leads to lower well productivity and higher operational costs. Adequate knowledge of coning phenomena and breakthrough time is essential to overcome this issue. A series of experiments using fractured porous media models were conducted to investigate the effects of production process and pore structure characteristics on water coning. In addition, a hybrid artificial neural network (ANN) with particle swarm optimization (PSO) algorithm was applied to predict breakthrough time of water coning as a function of production rate and physical model properties. Data from the literature combined with experimental data generated in this study were used to develop and verify the ANN‐PSO model. A good correlation was found between the predicted and real data sets having an absolute maximum error percentage less than 9%. The developed ANN‐PSO model is able to estimate breakthrough time and critical production rate with higher accuracy compared to the conventional or back propagation (BP) ANN (ANN‐BP) and common correlations. The presence of vertical fractures was found to accelerate considerably the water coning phenomena during oil production. Results of this study using combined data suggest the potential application of ANN‐PSO in predicting the water breakthrough time and critical production rate that are critical in designing and evaluating production strategies for naturally fractured reservoirs. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1905–1919, 2014     

Prediction of CO2-Oil Minimum Miscibility Pressure Using Soft Computing Methods

Searching for computational approaches for determination of the minimum miscibility pressure (MMP) is highly requested during the miscible gas injection process. New models, namely, the stochastic gradient boosting (SGB) algorithm and two distinct hybrid artificial neural network (ANN) models were used to predict CO₂ MMP as a function of reservoir temperature, mole percent of volatile oil components, mole percent of intermediate oil components, molecular weight of pentane-plus fraction in the oil phase, mole percentage of CO₂ in injected gas, volatile components, and intermediate components in the injected gas based on 144 published data points. The SGB model was found to provide the better performance. The reservoir temperature turned out to be the most important factor.     

Designing and Testing a Chemical Demulsifier Dosage Controller in a Crude Oil Desalting Plant: An Artificial Intelligence‐Based Network Approach

The aim of this paper is to present an artificial neural network (ANN) controller trained on a historical data set that covers a wide operating range of the fundamental parameters that affect the demulsifier dosage in a crude oil desalting process. The designed controller was tested and implemented on‐line in a gas‐oil separation plant. The results indicate that the current control strategy overinjects chemical demulsifier into the desalting process whereas the proposed ANN controller predicts a lower demulsifier dosage while keeping the salt content within its specification targets. Since an on‐line salt analyzer is not available in the desalting plant, an ANN based on historical measurements of the salt content in the desalting process was also developed. The results show that the predictions made by this ANN controller can be used as an on‐line strategy to predict and control the salt concentration in the treated oil.     

Simulation of hydrodesulfurization using artificial neural network

Artificial neural network (ANN) is applied to investigate the hydrodesulfurization (HDS) process with light‐cycle oil as feed and NiMo/Al₂O₃ as catalyst. ANN models frequently work as a “black box” which makes the model invisible to users and always need significant data for training. In this work, a new ANN is proposed. The Langmuir–Hinshelwood kinetic mechanism is incorporated into the model so that the proposed ANN model is forced to follow the given reaction mechanisms. Both advantages of self‐learning ability of ANN and the existing knowledge of HDS were taken into account. Lengthy training process is minimised. Effects of operating temperature, pressure, and LHSV on the sulfur removal rate are studied. The inhibition of nitrogen compounds is also investigated. It is shown that the presence of nitrogen can significantly reduce the conversion rate of sulfur components, in particularly, hard sulfur such as 4,6‐DMDBT.     

Asphaltene precipitation and deposition in oil reservoirs – Technical aspects,experimental and hybrid neural network predictive tools

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO₂ injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.     

Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self‐adaptive particle swarm optimization and a clustering method

A novel model based on a radial basis function neural network (RBF NN), chaos theory, self‐adaptive particle swarm optimization (PSO), and a clustering method is proposed to predict the gas solubility in polymers; this model is hereafter called CSPSO‐C RBF NN. To develop the CSPSO‐C RBF NN, the conventional PSO was modified with chaos theory and a self‐adaptive inertia weight factor to overcome its premature convergence problem. The classical k‐means clustering method was used to tune the hidden centers and radial basis function spreads, and the modified PSO algorithm was used to optimize the RBF NN connection weights. Then, the CSPSO‐C RBF NN was used to investigate the solubility of N₂ in polystyrene (PS) and CO₂ in PS, polypropylene, poly(butylene succinate), and poly(butylene succinate‐co‐adipate). The results obtained in this study indicate that the CSPSO‐C RBF NN was an effective method for predicting the gas solubility in polymers. In addition, compared with conventional RBF NN and PSO neural network, the CSPSO‐C RBF NN showed better performance. The values of the average relative deviation, squared correlation coefficient, and standard deviation were 0.1282, 0.9970, and 0.0115, respectively. The statistical data demonstrated that the CSPSO‐C RBF NN had excellent prediction capabilities with a high accuracy and a good correlation between the predicted values and the experimental data. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 3825–3832, 2013     

An investigation on the applicability of Parachor model for the prediction of MMP using five equations of state

Miscible gas injection is generally used as one of the most efficient methods in the enhanced oil recovery. Minimum miscibility pressure (MMP) is an important parameter in the miscible gas injection projects, since local displacement efficiency in the reservoir media is highly dependent on the MMP. Therefore, an appropriate estimation of MMP would bring significant economic benefits. This paper presents a comparative study on five representative equations of state (EoSs) for predicting MMP using Parachor model together with the criterion of zero interfacial tension (IFT) at the miscibility conditions. The predicted MMP values are compared with the experimental data obtained from the most reliable measurement technique, so called slim tube method. Such a prediction would enables us to judge the accuracy of the results obtained from different equations of state as well as the capability of Parachor model to calculate the MMP. The results of predictions obtained for five oil-gas systems in this study reveal reliable MMP values within 5% of accuracy.     

On fingering of steam chambers in steam‐assisted heavy oil recovery

Western Canadian oil sands reservoirs are among the largest petroleum accumulations in the world. Given original oil viscosity up to 5,000,000 mPa‐s, these oils are currently recovered from these reservoirs using steam which heats the oil to ～250°C with reduced viscosities <10 mPa‐s. A key issue faced by thermal recovery processes is the uniformity of the steam chamber within the reservoir. Nonuniformities of the chamber arise from multiphase flow instabilities in the porous media where fingering has been explained by penetration of low viscosity steam into viscous oil. Here, fine‐grid thermal reservoir simulation reveals that fingering takes place in the gas phase beyond the chamber edge in a zone created by gas exsolution due to elevated temperature beyond the edge of the steam chamber. The results suggest that nonuniform chambers will occur in perfectly homogeneous reservoirs which implies that uniform chambers along wells may be impossible to achieve. © 2015 American Institute of Chemical Engineers AIChE J, 62: 1364–1381, 2016     

高矿化度油气藏流体相平衡的模型化研究进展

深层油气资源开发面临高温、高压和高矿化度等复杂地质环境。地层中自然存在高浓度的盐水,在油水被采出的过程中,随着温度、压力和油水中溶解气体量的变化,盐类在储层中或在井筒中的沉淀所造成的储层伤害或井筒堵塞等问题是导致油气采收率降低的重要因素。目前,能描述高压高矿化度油气藏流体盐的沉淀问题的相平衡模型尚处于开发之中,采用电解质溶液基团贡献的活度因子模型来改进状态方程的混合规则是解决这一问题的途径之一。     

低渗透油藏CO_2驱油效果评价

针对低渗透油藏水驱采收率低,注水困难的特征,通过分析具体油藏的地质、储层及原油物性和最小混相压力等条件,确定了该油藏满足进行CO_2混相驱的要求。使用数值模拟软件Eclipse对该油藏进行模拟,对比连续注水、连续注气和周期注气三种开发方式,发现周期注气开发效果最好。当注停时间比为2:1时采出程度最高,分析其原因为注停时间比为2:1时,低渗透油藏能量的传播使地层压力重新均匀分布。对比不同CO_2驱替压力,发现当驱替压力在CO_2最小混相压力附近时采出程度最高,驱替压力大于最小混相压力,随着压力增大,采出程度越低,分析原因为储层发生堵塞现象。     

Oxidative Stability of Papaya Seed Oil From Hainan/Eksotika Obtained by Subcritical and Supercritical Carbon Dioxide Extraction

Papaya seed is a good source of edible oil with considerable antioxidant activity. Here, papaya seed oil (PSO) of Hainan/Eksotika variety was obtained by subcritical butane extraction (SBE) and supercritical carbon dioxide extraction (SCDE), and its yields, physicochemical properties, oxidative and thermal stability, and chemical and microscopic structures were compared. The results showed that SBE‐PSO had a higher yield (25.88 ± 0.29% vs. 19.47 ± 0.92%), but a lower melting point compared to SCDE‐PSO. Molecular structures indicated that there was no oxidative degradation during SBE and SCDE. Both SBE and SCDE caused significant structural changes of seed tissues. In addition, aldehyde composition analysis using high performance liquid chromatography (HPLC) showed that SBE‐PSO had fewer octanal, nonanal, 2‐decenal, and 2‐undecenal contents than SCDE‐PSO. All these results proved that SBE‐PSO exhibited superiority on the oxidative stability compared to SCDE‐PSO. It indicated that SBE was a superior method to obtain antioxidant edible oil with good stability.     

Structure and Stability of Carbohydrate–Lipid Interactions. Methylmannose Polysaccharide–Fatty Acid Complexes

We report a detailed study of the structure and stability of carbohydrate–lipid interactions. Complexes of a methylmannose polysaccharide (MMP) derivative and fatty acids (FAs) served as model systems. The dependence of solution affinities and gas‐phase dissociation activation energies (E_a) on FA length indicates a dominant role of carbohydrate–lipid interactions in stabilizing (MMP+FA) complexes. Solution ¹H NMR results reveal weak interactions between MMP methyl groups and FA acyl chain; MD simulations suggest the complexes are disordered. The contribution of FA methylene groups to the E_a is similar to that of heats of transfer of n‐alkanes from the gas phase to polar solvents, thus suggesting that MMP binds lipids through dipole‐induced dipole interactions. The MD results point to hydrophobic interactions and H‐bonds with the FA carboxyl group. Comparison of collision cross sections of deprotonated (MMP+FA) ions with MD structures suggests that the gaseous complexes are disordered.     

Neural network-based source tracking of chemical leaks with obstacles

The leakage of hazardous gases poses a significant threat to public security and causes environmental damage. The effective and accurate source term estimation (STE) is necessary when a leakage accident occurs. However, most research generally assumes that no obstacles exist near the leak source, which is inappropriate in practical applications. To solve this problem, we propose two different frameworks to emphasize STE with obstacles based on artificial neural network (ANN) and convolutional neural network (CNN). Firstly, we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark dataset. Secondly, we define the structure of ANN by searching, then predict the concentration distribution of gas using the searched model, and optimize source term parameters by particle swarm optimization (PSO) with well-performed cost functions. Thirdly, we propose a one-step STE method based on CNN, which establishes a link between the concentration distribution and the location of obstacles. Finally, we propose a novel data processing method to process sensor data, which maps the concentration information into feature channels. The comprehensive experiments illustrate the performance and efficiency of the proposed methods.     

Modeling and Optimization of Anode‐Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm

An artificial neural network (ANN) and a genetic algorithm (GA) are employed to model and optimize cell parameters to improve the performance of singular, intermediate‐temperature, solid oxide fuel cells (IT‐SOFCs). The ANN model uses a feed‐forward neural network with an error back‐propagation algorithm. The ANN is trained using experimental data as a black‐box without using physical models. The developed model is able to predict the performance of the SOFC. An optimization algorithm is utilized to select the optimal SOFC parameters. The optimal values of four cell parameters (anode support thickness, anode support porosity, electrolyte thickness, and functional layer cathode thickness) are determined by using the GA under different conditions. The results show that these optimum cell parameters deliver the highest maximum power density under different constraints on the anode support thickness, porosity, and electrolyte thickness.     

Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach

Precise modeling flux decline under various operating parameters in cross-flow ultrafiltration (UF) of oily wastewaters and afterward, employing an appropriate optimization algorithm in order to optimize operating parameters involved in the process model result in attaining desired permeate flux, is of fundamental great interest from an economical and technical point of view. Accordingly, this current research proposed a hybrid process modeling and optimization based on computational intelligence paradigms where the combination of artificial neural network (ANN) and genetic algorithm (GA) meets the challenge of specified-objective based on two steps: first the development of bio-inspired approach based on ANN, trained, validated and tested successfully with experimental data collected during the polyacrylonitrile (PAN) UF process to treat the oily wastewater of Tehran refinery in a laboratory scale in which the model received feed temperature (T), feed pH, trans-membrane pressure (TMP), cross-flow velocity (CFV), and filtration time as inputs; and gave permeate flux as an output. Subsequently, the 5-dimensional input space of the ANN model portraying process input variables was optimized by applying GA, with a view to realizing maximum or minimum process output variable. The results obtained validate the estimates of the ANN–GA technique with a good accuracy. Finally, the relative importance of the controllable operation factors on flux decline is determined by applying the various correlation statistic techniques. According to the result of the sensitivity analysis based on the correlation coefficient, the filtration time was the most significant one, followed by T, CFV, feed pH and TMP.     

Kinetic study of olefin polymerization with a supported metallocene catalyst. IV. Comparison of bridged and unbridged catalyst in gas phase

A kinetic study of ethylene/1‐hexene (E/1‐H) copolymerization is conducted with a supported bridged metallocene catalyst in a gas phase reactor. The investigation into the kinetics of ethylene/1‐hexene copolymerization includes the effects of operational parameters such as the reaction temperature, pressure, and comonomer concentration. On‐line perturbation techniques are implemented to determine key kinetic parameters such as the activation energies for propagation and catalyst deactivation. A comparison of the kinetic parameters and behavior is made between the bridged and a previously studied unbridged catalyst. Finally, a two‐site model is proposed to explain the observed kinetic behavior with changing reaction temperature and comonomer concentration. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 81: 1451–1459, 2001     

SOFT COMPUTING METHOD FOR PREDICTION OF CO2 CORROSION IN FLOW LINES BASED ON NEURAL NETWORK APPROACH

An important aspect of corrosion prediction for oil/gas wells and pipelines is to obtain a realistic estimate of the corrosion rate. Corrosion rate prediction involves developing a predictive model that utilizes commonly available operational parameters, existing lab/field data, and theoretical models to obtain realistic assessments of corrosion rates. This study presents a new model to predict corrosion rates by using artificial neural network (ANN) systems. The values of pH, velocity, temperature, and partial pressure of the CO₂ are input variables of the network and the rate of corrosion has been set as the network output. Among the 718 data sets, 503 of the data were implemented to find the best ANN structure, and 108 of the data that were not used in the development of the model were used to examine the reliability of this method. Statistical error analysis was used to evaluate the performance and the accuracy of the ANN system for predicting the rate of corrosion. It is shown that the predictions of this method are in acceptable agreement with experimental data, indicating the capability of the ANN for prediction of CO₂ corrosion rate in production flow lines.     

Simulation of shale gas transport and production with complex fractures using embedded discrete fracture model

The goal of this study is to develop a new model to simulate gas and water transport in shale nanopores and complex fractures. A new gas diffusivity equation was first derived to consider multiple important physical mechanisms such as gas desorption, gas slippage and diffusion, and non‐Darcy flow. For complex fractures, a state‐of‐the‐art embedded discrete fracture model (EDFM) was implemented. Numerical model is verified against a commercial reservoir simulator for shale gas simulation with multiple planar fractures. After that, a series of simulation studies was performed to investigate the impacts of complex gas transport mechanisms and various fracture geometries on well performance. The critical parameters controlling well performance are identified. The simulation results reveal that modeling of gas production from complex fractures as well as modeling important gas transport mechanisms in shale gas reservoirs is extremely significant. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2251–2264, 2018