A new model for predicting asphaltene precipitation of diluted crude oil by implementing LSSVM-CSA algorithm

Abstract

One of the severe problems in all the oil production stages from the pore walls of the reservoir rocks to the wellhead, transfer pipelines, and production units of a large portion of the world’s hydrocarbon reservoirs, is asphaltene precipitation and deposition from crude oil on solid surfaces. In this article, least squares support vector machine optimized by coupled simulated annealing is employed for estimation of the amount of asphaltene precipitated weight percent of diluted crude oil with paraffin based on titration tests data from a recently published article. The results indicated that there is an excellent correlation between predicted and experimental values with an average absolute relative deviation percent, mean square error, and a determination coefficient of 0.0727%, 0.0242, and 0.9972, respectively. The developed predicting model can be applied to estimate the amount of asphaltene precipitated when the crude oil is diluted with paraffin and to eschew experimental titration test that is tedious and time-consuming.     

A Thermodynamic Model for the Prediction of Asphaltene Precipitation

Abstract

Asphaltene precipitation in reservoirs, wells, and facilities can have a severe and detrimental impact on the oil production. Due to the extreme chemical complexity of the asphaltene and crude oil and the lack of comprehensive experimental data, the modeling of asphaltene precipitation in crude oil remains as a challenging task. In this article, a compositional thermodynamic model was developed to predict asphaltene precipitation conditions. The proposed model is based on a cubic equation of state with an additional term to describe the association of asphaltene molecules. Extensive testing against the literature data, including asphaltene precipitation from crude oil and solvent injection systems, concludes that the proposed model provides reasonable predictive results.     

原油正构烷烃沥青质聚沉机理研究及沉淀量测定

用IP 143标准方法测定了我国孤岛和草桥原油正构烷沥青质沉淀量。结果表明两种原油的沥青质沉淀量均随沉淀剂分子量增大而减小、随剂油比增大而增大。在原油沥青质 胶质胶束模型的基础上提出了一种新的沥青质聚沉机理 ,该机理的基本假设是原油中沥青质分子以胶束形式存在 ,其中胶核为沥青质缔合物 ,溶剂化层为胶质和溶剂分子。通过分析沉淀剂性质、剂油比、体系温度和压力等对沥青质 胶质胶束稳定性的影响得出了沥青质沉淀点、沉淀量、沉淀物平均分子量以及沉淀物平均颗粒大小随沉淀剂性质和剂油比等因素变化的规律。经比较说明 ,这些规律与本文及文献实验结果相符     

Utilization of RBF-ANN as a novel approach for estimation of asphaltene inhibition efficiency

One of the problematic concerns in petroleum industries is the deposition of heavy fractions of crude oil such as asphaltene fraction during production and transportation. The utilization of inhibitors is known as a relative low cost and effective method for asphaltene inhibition. In this study, Radial basis function artificial neural network (RBF-ANN) was applied to predict asphaltene precipitation reduction in terms of structure and concentration of inhibitor and oil properties. In order to training and testing of RBF-ANN the required data are extracted from reliable sources. The predicted asphaltene precipitation reduction values were compared with the actual data statistically and graphically. The coefficients of determination for training and testing phases of RBF-ANN were determined as 0.995906 and 0.994853 respectively. These evaluations showed that the RBF-ANN as a predictive tool has great capacity to estimate effect of asphaltene inhibitors on reduction of asphaltene precipitation.     

Developing a Scaling Equation as a Function of Pressure and Temperature to Determine the Amount of Asphaltene Precipitation

A simple and applicable scaling equation as a function of pressure, temperature, molecular weight, dilution ratio (solvent), and weight percent of precipitated asphaltene has been developed. This equation can be used to determine the weight percent of precipitated asphaltene in the presence of difference precipitants (solvents) and the amount of solvent at onset point. Since increasing the pressure of crude oil decreases the amount of asphaltene precipitation, the effect of reservoir pressure has been taken into account in developing this equation. The results obtained by using this equation are substantially different and more accurate from other developed scaling equations for asphaltene precipitation. By considering the effect of reservoir pressure in developing the scaling equation and application of a genetic algorithm, the unknown parameters of the scaling equation are simultaneously and without any reservation obtained. The most important application of this unique equation is in the determination of critical point of asphaltene precipitation, known as onset point, and asphaltene precipitation in gas injection operations for enhanced oil recovery. The results predicted using the scaling equations are compared with the authors' experimental and literature precipitation data and it is shown that they are in good agreement with our experimental data. The scaling equation can be used in the design of gas-injected reservoir to prevent precipitation of the asphaltene aggregates in the reservoir.     

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.     

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.     

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.     

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.     

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.     

The Impact of CO2 Injection and Pressure Changes on Asphaltene Molecular Weight Distribution in a Heavy Crude Oil: An Experimental Study

Abstract

This work concerns observing the pressure as well as CO₂ mole percentage effects on asphaltene molecular weight distributions at reservoir conditions. A high-pressure, high-temperature asphaltene measurement setup was applied, and the amount of precipitated asphaltene at different pressures as well as CO₂ mole percentage in an Iranian heavy crude oil was measured. Moreover, the asphaltene molecular weight distributions during titration of crude oil with different n-alkanes were investigated. The gel permeation chromatography (GPC) apparatus was used for characterization of asphaltene molecular weight under different conditions. It has been observed that some thermodynamic changes such as pressure depletion above the bubble point increase the average molecular weight of asphaltene and cause the asphaltene molecular weight distributions changes from a bimodal curve with two maxima to a single maxima curve. One the other hand, below the bubble point, pressure reduction causes a decrease in the average molecular weight of asphaltene and also causes the shape of asphaltene molecular weight distributions to restore, which might be due to dissolution of asphaltene aggregates. An interesting result is that asphaltene molecular weight distribution at the final step of pressure reduction tests, ambient condition, shows approximately the same trend as the distribution of asphaltene molecular weight obtained at reservoir condition. This behavior explains the reversibility of the asphaltene precipitation process under pressure depletion conditions. In the case of CO₂ injection, the graphs of asphaltene molecular weight distributions always show a single modal trend and shift toward larger molecular weight values when CO₂ mole percentage increases. The results of this work can be imported to thermodynamic models that use polydisperse data of heavy organic fractions to enhance their performance at reservoir conditions. The distributions obtained by this method are good indicators of asphaltene structures at reservoir conditions.     

Modeling Formation Damage Due to Flocculated Asphaltene Deposition

Abstract

In this article, a hybrid model of an analytical and artificial neural network simulation and corresponding analytical method are applied using laboratory data obtained by performing various dynamic displacement experiments with preseparated oil asphaltene content that resulted a close agreement, so it could predict the trend of permeability reduction due to deposition of asphaltene using the hybrid model described. The procedure of matching is described here.

The main conclusion is the ability to predict the deposition of asphaltene in the reservoir without the need to generate data from expensive downhole samples and/or laboratory tests.     

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.     

The Structural Characterization of Saturate,Aromatic, Resin,and Asphaltene Fractions of Batiraman Crude Oil

Abstract

The structural characterization of fractions of Batiraman crude oil, which is the heavy crude oil from a field in the southeastern part of Turkey, was investigated. Batiraman crude oil and its saturate, aromatic, resin, and asphaltene (SARA) fractions were seperated. Treatment of crude oil with n-heptane provided the separation of asphaltene. Maltene was collected by evaporating the n-heptane from the filtrate. Then, maltene was separeted into saturates, aromatics, and resins by SARA technique. Maltene was separated into saturate, aromatic, and resin fractions using column chromatography. SARA fractions were quantified on a weight percent basis. Fractions of Batiraman crude oil were characterized by elemental analysis, proton nuclear magnetic resonance (¹H NMR) analysis, electrospray ionization mass spectrometry (ESI-MS), and Fourier transform infrared (FTIR) spectroscopy techniques.     

EFFECT OF ASPHALTENE AND RESINS ON THE STABILITY OF WATER-IN-WAXY OIL EMULSIONS

ABSTRACT

Asphaltene, resins and paraffin waxes, their mutual interactions and their influence on the stability of water-in-oil emulsions have been studied. 20 wt % paraffin wax dissolved in decalin was used to model the waxy crude oil. Asphaltene and resins separated from a crude oil were used to stabilize the water-in-oil emulsions. Synthetic formation water was utilized as the aqueous phase of the emulsion. The emulsion stability increased with increasing the concentration of asphaltene with a subsequent decrease in the average particle size distribution of the emulsion. Resins alone are not capable of stabilizing the emulsion, however, in the presence of asphaltene they form very stable emulsions. Dynamic viscosity and pour point measurements provided evidence for resins-paraffin waxes interactions. Asphaltene in the form of solid aggregates form suitable nuclei for the wax crystallites to build over with a mechanism similar to that of paraffin wax crystal-modifiers. As asphaltene are polar in nature they are derived at the oil/water interface which was proved by the ability of asphaltene to reduce oil/water interfacial tension. Consequently, nucleation of the wax crystallites by asphaltene and resins at the interface will add to the thickness of the oil-water interfacial film and hence increase the stability of the emulsion.     

MICROSCOPIC INVESTIGATION OF THE ONSET OF ASPHALTENE PRECIPITATION

ABSTRACT

A microscopic study of the onset of asphaltene precipitation is reported. The onset conditions can be quantified by measurement of mixture refractive index, together with microscopic observations of particulate formation in mixtures of oil and precipitant, with or without added solvents. For isooctane mixtures with a variety of hydrocarbon solvents and a crude oil from Alaska, the onset of precipitation occurs over a narrow range of solution refractive index. Addition of polar solvents or different precipitating agents can shift the refractive index at which precipitation begins. Refractive index decreases when a crude oil is diluted by precipitant, as in this study, or when changes in temperature and pressure alter the relative molar volumes of species in the oil. If it falls below some critical value, resin/asphaltene aggregates that had been in stable dispersion become unstable and precipitate. These observations provide a method of screening solvents to differentiate between those that prevent precipitation mainly by maintaining a higher mixture refractive index and others that may participate in or disrupt asphaltene/resin interactions.     

Prediction of asphaltene precipitation from n-alkane diluted crude oil by using PC-SAFT equation of state

The precipitation tendency of heavy organics such as asphaltene has posed great challenges for petroleum industry, and thus study of asphaltene precipitation amount and formation conditions seems to be necessary. One of the most common approaches for prediction of asphaltene precipitation is using thermodynamic models. In this study a PC-SAFT equation of state (EOS) is used to predict asphaltene precipitation in two Iranian dead oil samples. Asphaltene content is obtained by filtration method of the oil samples diluted with specific concentrations of different normal alkanes. Also liquid-liquid equilibrium is used for characterization of oil sample into one heavy phase (asphaltene) and another light phase (saturates, aromatics, and resin). Calculations show that the developed model is highly sensitive to interaction parameter between oil fractions. Prediction results were improved due to using Chueh-Prausnitz equation. The results indicate good potential of PC-SAFT EOS in the prediction of asphaltene precipitation in crude oil samples diluted with different normal alkanes. The model error is <5% and the model precision is increased by reducing the number of normal alkane carbons.