Prediction of asphaltene precipitation during gas injection

Maintaining the flow of multiphase fluid from the reservoir to the surface has been an important issue with wide economic importance for the petroleum industry. Asphaltene precipitation due to change in temperature, pressure, and composition of oil can adversely affect the oil flow to the surface by reducing the available diameter of the tubing. In this study, the precipitation of asphaltene from an Iranian crude oil was investigated. To do our study, through information about asphaltene instability in the live oil during both natural depletion and gas injection conditions about oil sample from Iranian oil field was gathered. Then, the solid model and scaling model were utilized to predict the weight percent of precipitated asphaltene at a wide range of the pressure and temperature. Results of the work revealed that both models predict the increase in weight percent of precipitated asphaltene when lean gas injected to the live oil at the maximum point of asphaltene instability. In addition, the study showed that both models are capable of predicting the experimental data of asphaltene precipitation; while scaling modeling is more reliable when the gas is injected to the oil.     

Prediction of Asphaltene Precipitation during Pressure Depletion and CO2 Injection for Heavy Crude

Abstract

In this work, a thermodynamic approach is used for modeling the phase behavior of asphaltene precipitation. The precipitated asphaltene phase is represented by an improved solid model, and the oil and gas phases are modeled with an equation of state. The Peng-Robinson equation of state (PR-EOS) was used to perform flash calculations. Then, the onset point and the amount of precipitated asphaltene were predicted. A computer code based on the solid model was developed and used for predicting asphaltene precipitation data reported in the literature as well as the experimental data obtained from high-pressure, high-temperature asphaltene precipitation experiments performed on Sarvak reservoir crude, one of Iranian heavy oil reserves, under pressure depletion and CO₂ injection conditions. The model parameters, obtained from sensitivity analysis, were applied in the thermodynamic model. It has been found that the solid model results describe the experimental data reasonably well under pressure depletion conditions. Also, a significant improvement has been observed in predicting the asphaltene precipitation data under gas injection conditions. In particular, for the maximum value of asphaltene precipitation and for the trend of the curve after the peak point, good agreement was observed, which could not be found in the available literature.     

Predicting asphaltene precipitation during titration of diluted crude oil with paraffin using artificial neural network (ANN)

Abstract

Asphaltene precipitation from crude oil in underground reservoirs and on ground facilities is one of the major problems in a large portion of oil production units around the world. Many scaling equations and intelligent predictive models using the artificial neural network (ANN) are proposed in the literature but none of them can be applied when crude oil is diluted with any types of paraffin. In this study, feed forward artificial neural network is used for prediction of the amount of asphaltene precipitated weight percent of diluted crude oil with paraffin based on titration tests data from published literature. Trial and error method is utilized to optimize the artificial neural network topology in order to enhance its strength of generalization. The results showed that there is good agreement between experimental and predicted values. This predictive model can be applied to estimate the amount of asphaltene precipitated weight percent when the crude oil is diluted with paraffin and to avoid experimental measurement that is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure.     

Application of Radial Basis Function Neural Network for Prediction of Titration-Based Asphaltene Precipitation

The precipitation of asphaltene, a polar fraction of crude oil, during oil production has unfavorable impacts on many parts of the petroleum industry. Within the upstream processes, asphaltene precipitation occurs in crude oil, forming solid deposits in the reservoir during enhanced oil recovery operations and natural depletion. This significantly influences the porosity and permeability of the reservoir, reducing the effectiveness of the recovery process. Precipitation and deposition in downstream processes causes noticeable increases in production costs. Therefore, it is essential to predict the amount of asphaltene precipitation based on pressure, temperature and liquid phase composition using a dependable, precise, and robust strategy. However, the experimental measurement techniques used to estimate amounts are expensive and time consuming, while the thermodynamic models available are also somewhat complex. The authors propose an innovative approach for the simple and prompt prediction of asphaltene precipitation, employing an artificial neural network. The results show that the predicted values were in agreement with the experimental data, with the maximum absolute error deviation for the proposed model no more than 2.46%. A comparison of the proposed model with previously presented models highlight the superiority of the model developed in this study.     

Experimental and Modelling Investigations of Asphaltene Precipitation During Pressure Depletion and Gas Injection Operations

Asphaltene precipitation problems manifest themselves in different stages of oil reservoirs production. Experimental and modeling investigations are, therefore, employed as promising tools to assist in predictions of asphaltene precipitation problems and selection of proper production facilities. This study concerns experimental and modeling investigations of asphaltene precipitation during natural production and gas injection operations for a heavy Iranian crude oil at reservoir conditions. First, with design and performance of high pressure–high temperature experiments, asphaltene precipitation behavior is comprehensively investigated; the effects of pressure and temperature are fully studied during pressure depletion tests and the role of injection gas composition on precipitation is described in gas injection experiments. In the next stage, the obtained experimental results are fed into a commercial simulator to develop the asphaltene precipitation model. The results for the pressure depletion experiments indicate that the maximum amount of asphaltene precipitation takes place at fluid bubble point pressure. Increase in the temperature, as seen, causes to reduce the amount of precipitation for the entire range of pressures. For gas injection experiments, the onset of precipitation for CO₂, associated, and N₂ gases takes place at around 0.20, 0.28, and 0.50 gas to mixture mole ratios, respectively. Carbon dioxide shows the highest asphaltene precipitation values and nitrogen has the lowest amounts for the whole range of gas mole fractions. Finally, the results for modeling indicate successful asphaltene precipitation predictions for both pressure depletion and gas injection processes.     

Monitoring of asphaltene precipitation: Experimental and modeling study

Preparing relatively complete collections of experimental data on asphaltene precipitation in different reservoir conditions leads to considerable improvement in this area of science. In this work, asphaltene precipitation was studied upon two Iranian live oil samples, one a heavy oil and another light oil, under primary depletion as well as gas injections. Pressure depletion experiments were carried out at different temperatures to observe temperature effect besides pressure changes on asphaltene phase behavior. CO₂, dry and enriched gases were used as injecting agents to investigate the effect of different gases on asphaltene precipitation. Surprisingly, it was observed that raising temperature decreases the amount of precipitation in case of heavy oil while acting in favor of precipitation for light oil sample. In addition, Enriched gas resulted in more precipitation compared to dry one while CO₂ acted as hindering agent for light oil samples but increased the amount of precipitation in case of heavy oil. In the next part of this work, polydisperse thermodynamic model was developed by introducing an asphaltene molecular weight distribution function based on fractal aggregation. Modification that was introduced into polydisperse model not only solved the instability problem of Kawanaka model but also eliminates the need for resin concentration calculation. Flory–Huggins and Modified Flory–Huggins thermodynamic solubility models were applied to compare their predictions with proposed model.     

高压注烃类气体过程中沥青质初始沉淀压力试验研究

为预防注烃类气体提高采收率过程中产生沥青质沉淀,对沥青质初始沉淀压力进行了试验研究.在分析注烃类气体过程中沥青质沉淀机理的基础上,通过自主研发的固相沉积激光探测装置,采用透光强度法测定了原油样品在不同温度下高压注气过程中沥青质的初始沉淀压力,并确定了沥青质沉淀的深度.试验得出,原油沥青质初始沉淀压力随温度升高而下降,测得44,80和123 ℃温度下原油的沥青质初始沉淀压力分别为44.1,39.7和35.2 MPa;每注入物质的量分数为1%的烃类气体,试验油样的沥青质初始沉淀压力升高0.5~0.6 MPa;井筒温度压力曲线与沥青质沉淀相包络线相结合预测井筒中出现沥青质沉淀的深度在1 800 m左右,与现场情况吻合较好.研究表明,原油中沥青质初始沉淀压力与注气量之间呈线性关系,可为现场注气驱油预防和清除沥青质沉积物提供理论依据.      

Simulation of Natural Depletion and Miscible Gas Injection Effects on Asphaltene Stability in Petroleum Reservoir Fluids

Natural depletion of petroleum reservoirs as well as gas injection for enhance oil recovery, are unavoidable processes in the oil industry. Foremost, prediction of the problems due to these two processes is very necessary and important. So many field and experimental experiences have shown that heavy organic depositions, especially asphaltene deposition, are principal results during these processes. Results of laboratory simulation of asphaltene deposition during the natural depletion of petroleum reservoirs and also during gas injection and enhanced oil recovery (EOR) processes are reported here. This is achieved through the design of a new experimental setup for the investigation of pressure and composition effects on asphaltene deposition in petroleum fluids at high pressure and high temperature conditions. In this work, asphaltene deposition during decreasing pressure, from pressures greater than reservoir pressure to pressures below the bubble point pressure (natural depletion) and also asphaltene deposition during natural gas injection in reservoir conditions, are studied for three samples—one recombined sample and two bottomhole samples. All of the obtained results from this work conform to theoretical and other experimental works.     

Simulation of Natural Depletion and Miscible Gas Injection Effects on Asphaltene Stability in Petroleum Reservoir Fluids

Abstract

Natural depletion of petroleum reservoirs as well as gas injection for enhance oil recovery, are unavoidable processes in the oil industry. Foremost, prediction of the problems due to these two processes is very necessary and important. So many field and experimental experiences have shown that heavy organic depositions, especially asphaltene deposition, are principal results during these processes. Results of laboratory simulation of asphaltene deposition during the natural depletion of petroleum reservoirs and also during gas injection and enhanced oil recovery (EOR) processes are reported here. This is achieved through the design of a new experimental setup for the investigation of pressure and composition effects on asphaltene deposition in petroleum fluids at high pressure and high temperature conditions. In this work, asphaltene deposition during decreasing pressure, from pressures greater than reservoir pressure to pressures below the bubble point pressure (natural depletion) and also asphaltene deposition during natural gas injection in reservoir conditions, are studied for three samples—one recombined sample and two bottomhole samples. All of the obtained results from this work conform to theoretical and other experimental works.     

An Investigation of Asphaltene Precipitation During Natural Production and the CO2 Injection Process

Some of Iranian oil reservoirs suffer from operational problems due to asphaltene precipitation during natural depletion, so widely investigation on asphaltene precipitation is necessary for these reservoirs. In this study, a reservoir that is candidate for CO₂ gas injection process is selected to investigate asphaltene precipitation with and without CO₂ injection. In this case, asphaltene precipitation is monitored at various pressures and reservoir temperature. Then, a series of experiments are carried out to evaluate the amount of precipitated asphaltene by injection different molar concentrations (25%, 50%, and 75%) of CO₂. The results show that during primary depletion the amount of precipitated asphaltene increases with pressure reduction until bubble point pressure. Below the bubble point the process is reversed (i.e., the amount of precipitated asphaltene at bubble point pressure is maximum). The behavior of asphaltene precipitation versus pressure for different concentrations of CO₂ is similar to primary depletion. Asphaltene precipitation increases with CO₂ concentration at each pressure step. In the modeling part, solid model and Peng-Robinson equation of state are employed which show a good match with experimental results.     

原油沥青质初始沉淀压力测定与模型化计算

温度、压力及组成的改变均会造成原油中沥青质产生沉淀,导致储层伤害和井筒堵塞。文中通过自主研制的固相沉淀激光探测系统,用透光率法首次测定了伊朗南阿油田原油样品在不同温度下的沥青质初始沉淀压力;同时利用Nghiem等建立的沥青质沉淀预测的热力学模型对油样沥青质初始沉淀压力进行计算,并与实验结果拟合。结果表明：利用透光率法测定该油田油样,在44,80,123℃下的沥青质初始沉淀压力点分别为42.8,39.7,35.2 MPa;沥青质初始沉淀压力随着温度的升高,在井筒温度范围内呈线性关系。模型计算与实验结果误差不超过15%,所以利用Nghiem模型对原油沥青质的初始沉淀压力进行预测是可靠的。     

注天然气油井沥青质析出规律研究

目的 解决东河区块原油在注气开采过程中沥青质沉积堵塞井筒问题。方法 采用高温高压固相沉积规律测试装置,基于光散射理论,研究了温度、压力、气油比等因素对沥青质析出特征的影响。结果 温度升高会增加沥青质在原油中的溶解度,促进原油稳定;等温降压过程中,沥青质随着压力降低逐渐析出,在泡点压力附近达到最大析出量,发生沥青质沉积堵塞油井的风险最大。DH-1井泡点压力对应井深2 140 m,与油井生产实际遇阻位置1 969 m接近,泡点压力可初步用于预测油井堵塞位置;溶解注气量越大,沥青质初始析出压力越大,沥青质析出压力区间也增大,沥青质沉积位置向油井深度下移。结论 研究揭示了注气过程沥青质的析出规律,对注天然气油井沥青质析出防治具有重要指导作用。     

The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs

Abstract

The authors introduce a new implementation of the neural network (ANN), genetic programming neural network (GPNN), and neuro-fuzzy (NF) technology in petroleum engineering. An intelligent framework is developed for calculating the amount of permeability reduction by asphaltene precipitation in Iranian crude oil reservoirs over a wide pressure, temperature, and solvent mole fraction range. Theoretical results and practical experience indicate that a feed-forward network can approximate a wide class of function relationships very well. In this work, a conventional feed-forward multilayer ANN, GPNN, and NF approach have been proposed to predict the amount of permeability reduction. The accuracy of the method is evaluated by predicting the amount of permeability reduction of various reservoir fluids not used in the development of the models. One of the ways in modeling such systems is using intelligent techniques, which need information about the systems, so, based on some intelligent learning methods, it can provide a suitable model. Furthermore, the performance of the model is compared with the performance of a simple model for permeability reduction prediction, a new correlation, and experimental data. Results of this comparison show that the proposed GPNN method first and then NF method is superior both in accuracy and generality, over the other models.     

Prediction of Oil Recovery Factor in CO2 Injection Process

In this study, prediction of recovery factor (RF) for CO₂ injection into oil reservoirs based on artificial neural networks (ANNs) and mathematical models were investigated. To design the optimum ANN model, number of neurons, hidden layers, and training function were studied. Finally, efficiency of the models was evaluated using new data. As a result of this work, it can be concluded that it is possible to predict RF in CO₂ injection process by ANN and mathematical model. However, values that obtained from ANN were in the best agreement with the actual values than regression model. The proposed artificial neural network predicted RF during CO₂ injection with error about 0.396%.     

Asphaltene Precipitation During Different Production Operations

Asphaltene precipitation due to enhanced oil recovery (EOR) methods or natural depletion is a serious technical problem at petroleum industry. The authors present the result of asphaltene precipitation during associated gas injection, CO₂ injection, and natural depletion in reservoir condition. In addition, the effect of variations in operation pressure, injection gas concentration, and production rate on asphaltene precipitation and difference between slope of precipitation graph due to various method of EOR or natural depletion were investigated. The results revealed that temperature has an efficient role on result of asphaltene deposition through associated gas and CO₂ injection. By decreasing temperature, the amount of asphaltene precipitation due to associated gas injection was increased. In fact, recovery of gas injection was decreased at lower temperatures, hence; solubility has an important rule on asphaltene precipitation.     

Development of an Artificial Neural Network Algorithm for the Prediction of Asphaltene Precipitation

Abstract

The precipitation and deposition of crude oil polar fractions such as asphaltenes in petroleum reservoirs considerably reduce rock permeability and oil recovery. Therefore, it is of great importance to determine how and how much the asphaltenes precipitate as a function of pressure, temperature, and liquid phase composition. The authors designed and applied an Artificial Neural Network (ANN) model to predict the amount of asphaltene precipitation at a given operating condition. Among this training, the back-propagation learning algorithm with different training methods was used. The most suitable algorithm with an appropriate number of neurons in the hidden layer, which provides the minimum error, was found to be the Levenberg-Marquardt (LM) algorithm. An extensive experimental data for the amount of asphaltene precipitation at various temperatures (293–343 K) was used to create the input and target data for generating the ANN model. The predicted results of asphaltene precipitation from the ANN model was also compared with the results of proposed scaling equations in the literature. The results revealed that scaling equations cannot predict the amount of asphaltene precipitation adequately. With an acceptable quantitative and qualitative agreement between experimental data and predicted amount of asphaltene precipitation for all ranges of dilution ratio, solvent molecular weight and temperature was obtained through using ANN model.     

Intelligent Modeling of Asphaltene Precipitation in Live and Tank Crude Oil Systems

Abstract

The study of asphaltene precipitation properties has been motivated by their propensity to aggregate, flocculate, precipitate, and adsorb onto interfaces. The tendency of asphaltenes to precipitation has posed great challenges for the petroleum industry. The most important parameters in asphaltene precipitation modeling and prediction are the asphaltene and oil solvent solubility parameters, which are very sensitive to reservoir and operational conditions. The driving force of asphaltene flocculation is the difference between asphaltene and the oil solvent solubility parameter. Since the nature of asphaltene solubility is yet unknown and several unmodeled dynamics are hidden in the original systems, the existing prediction models may fail in prediction the asphaltene precipitation in crude oil systems. One of ways in modeling such systems is using intelligent techniques that need some information about the systems; so, based on some intelligent learning methods it can provide a suitable model. The authors introduce a new implementation of the artificial intelligent computing technology in petroleum engineering. They have proposed a new approach to prediction of the asphaltene precipitation in crude oil systems using fuzzy logic, neural networks, and genetic algorithms. Results of this research indicate that the proposed prediction model with recognizing the possible patterns between input and output variables can successfully predict and model asphaltene precipitation in tank and live crude oils with a good accuracy.     

An Experimental Investigation of Asphaltene Precipitation During Natural Production of Heavy and Light Oil Reservoirs: The Role of Pressure and Temperature

Abstract

Many oil reservoirs encounter asphaltene precipitation as a major problem during natural production. In spite of numerous experimental studies, the effect of temperature on asphaltene precipitation during pressure depletion at reservoir conditions is still obscure in the literature. To study their asphaltene precipitation behavior at different temperatures, two Iranian light and heavy live oil samples were selected. First, different screening criteria were applied to evaluate asphaltene instability of the selected reservoirs using pressure, volume, and temperature data. Then, a high pressure, high temperature filtration (HPHT) setup was designed to investigate the asphaltene precipitation behavior of the crude samples throughout the pressure depletion process. The performed HPHT tests at different temperature levels provided valuable data and illuminated the role of temperature on precipitation. In the final stage, the obtained data were fed into a commercial simulator for modeling and predicting purposes of asphaltene precipitation at different conditions. The results of the instability analysis illustrated precipitation possibilities for both reservoirs which are in agreement with the oil field observations. It is observed from experimental results that by increasing the temperature, the amount of precipitated asphaltene in light oil will increase, although it decreases precipitation for the heavy crude. The role of temperature is shown to be more significant for the light crude and more illuminated at lower pressures for both crude oils. The results of thermodynamic modeling proved reliable applicability of the software for predicting asphaltene precipitation under pressure depletion conditions. This study attempts to reveal the complicated role of temperature changes on asphaltene precipitation behavior for different reservoir crudes during natural production.     

Prediction of asphaltene precipitation using non-isothermal compositional network model

In this paper a comprehensive flow model which incorporates compositional and non-isothermal effects is proposed to investigate asphaltene precipitation onset conditions in advanced well completions. The focus is on precipitation induced by pressure and temperature conditions, particularly in flow restrictions used in wells to delay unwanted break through of water/gas. A network model is used with a non-isothermal black oil fluid model to predict the distribution of pressure, temperature, flow rate and phase fractions in all components of the well completion. The network geometry consists of a production tubing (or liner) and an annulus between the reservoir and the tubing. This geometry will allow for flow between the annulus and the tubing through inflow control devices which are commonly used for zonal control. An asphaltene precipitation envelope is used to identify locations in the well completion at risk. Subsequently, a fully compositional and non-isothermal model is invoked at these locations. This detailed model uses a Finite Difference representation of conservation of mass, energy and momentum. Furthermore, it uses an isenthalpic pseudo-three-phase equilibrium model to predict if asphaltene precipitation actually will occur inside the restriction. A case study is presented in which the proposed model was successfully used to predict physical flow parameters and asphaltene onset conditions. It was found that asphaltene precipitation may occur in flow restriction due to large pressure drop. Furthermore, it was found that the use of isothermal modeling to predict asphaltene precipitation may lead to underestimation of the precipitation. It is concluded that the details of the well completion must be represented in the flow model since pressure and temperature may vary non-monotonically from toe to heel in advanced well completions.     

Prediction of asphaltene precipitation in crude oil

Asphaltene are problematic substances for heavy-oil upgrading processes. Deposition of complex and heavy organic compounds, which exist in petroleum crude oil, can cause a lot of problems. In this work an Artificial Neural Networks (ANN) approach for estimation of asphaltene precipitation has been proposed. Among this training the back-propagation learning algorithm with different training methods were used. The most suitable algorithm with appropriate number of neurons in the hidden layer which provides the minimum error is found to be the Levenberg–Marquardt (LM) algorithm. ANN's results showed the best estimation performance for the prediction of the asphaltene precipitation. The required data were collected and after pre-treating was used for training of ANN. The performance of the best obtained network was checked by its generalization ability in predicting 1/3 of the unseen data. Excellent predictions with maximum Mean Square Error (MSE) of 0.2787 were observed. The results show ANN capability to predict the measured data. ANN model performance is also compared with the Flory–Huggins and the modified Flory–Huggins thermo dynamical models. The comparison confirms the superiority of the ANN model.