Analyzing electrical field effect on asphaltene deposition

Asphaltenes are the heaviest and most complicated fraction in a crude oil sample and consist of condensed polynuclear aromatics, small amounts of heteroatoms (e.g., S, N, and O), and some traces of metal elements (e.g., nickel and vanadium). The main mechanisms of asphaltene deposition are precipitation (formation of asphaltene solids out of liquid phase), aggregation (formation of larger asphaltene particles), and deposition (adsorption and adhesion onto the surface). Asphaltene deposition is a major unresolved flow assurance problem in the petroleum industry, which may occur anywhere in the production system consists of reservoir, wellbore, through flowing and the separator. Asphaltene moieties in crude oil are found to carry residual surface electric charge, so by exerting an electrical field in a specific length of pipe, asphaltenes will deposit and we will have no blockage problem. Determining asphaltene electric charge is an important issue that will be done by static experiment, and then effect of electrical field on asphaltene deposition in dynamic state should be investigated. This paper discusses electric field effect on asphaltene deposition and represents a way to deposit asphaltene moieties in specific location.     

Parametric analysis of the effect of operation parameters on asphaltene deposition: An experimental study

Changing pressure, temperature, and composition cause instability in crude oil and create a problematic issue which is called asphaltene deposition. Asphaltene deposition causes problems in wettability alteration and flow assurance in different parts of petroleum industry so asphaltene deposition becomes a challenging issue in petroleum engineering. Hence, it is necessary to predict asphaltene deposition and investigate parameters which effect on asphaltene deposition. In this contribution, because of similarity between pore throat of reservoir rock and capillary tube, to investigate parameters such as asphaltene content, precipitant ratio, flow rate, and temperature effect on asphaltene deposition, a capillary setup was constructed and a model was developed to relate pressure drop along capillary tube to permeability reduction.     

Development of a novel ANFIS-GA method for investigation of effect of inhibitors on asphaltene precipitation

Asphaltene precipitation and deposition in different parts of petroleum industry increase considerably cost and problems in oil production. Due to these facts controlling asphaltene precipitation becomes one of valuable topics for research in petroleum engineering. Utilization of Asphaltene inhibitors is known as one of the dominant methods for controlling asphaltene precipitation so in this paper Adaptive neuro-fuzzy inference system (ANFIS) is joint with Genetic Algorithm (GA) to study effectiveness of asphaltene inhibitors on precipitation in terms of oil and inhibitors properties. In order to prepare and evaluate the ANFIS-GA algorithm, some reliable experimental data were gathered. The obtained results from the comparison shows the coefficient of determination (R²) for training and testing phases are 0.98804 and 0.9916 respectively. The determined indexes and graphical comparisons expresses that ANFIS-GA has enough accuracy and potential to estimate effectiveness of inhibitors on asphaltene precipitation reductions.     

Experimental investigation of dodecylbenzene sulfonic acid and toluene dispersants on asphaltene precipitation of dead and live oil

Asphaltene is the heaviest fraction of oil, and if the thermodynamic conditions of oil change, it can be separated from oil precipitate. Of common methods for preventing asphaltene precipitation, using predictive methods, biological methods and injection of dispersants can be mentioned. In this study, the effect of two dispersants of toluene and dodecylbenzene sulfonic acid on asphaltene precipitation of a dead and a live oil sample has been investigated. According to the results, these dispersants in dead oil create an optimum point for asphaltene precipitation. In live oil, these dispersants reduce asphaltene precipitation down to 70%. In addition, it was observed that as an effect of injecting these dispersants, the average sizes of asphaltene flocculation have reduced.     

Application of MLP-ANN as novel tool for estimation of effect of inhibitors on asphaltene precipitation reduction

Sedimentation of heavy fractions of oil such as asphaltene is the main concern in different parts of petroleum industries like production and transportation. Due to this fact, the inhibition of asphaltene precipitation becomes one of the great interests in the petroleum industry. In the present investigation, multi-layer perceptron artificial neural network (MLP-ANN) was developed to estimate asphaltene precipitation reduction as a function of concentration and kind of inhibitors and oil properties. To this end, a total number of 75 data points were extracted from reliable source for training and validation of predicting algorithm. The outputs of MLP-ANN were compared with experimental data graphically and statistically, the determined coefficients of determination (R²) for training and testing are 0.996522 and 0.995239 respectively. These comparisons expressed the high quality of this algorithm in the prediction of asphaltene precipitation reduction. so the MLP-ANN can be used as a powerful machine for estimation of different processes in petroleum industries.     

A novel MLP approach for estimating asphaltene content of crude oil

ABSTRACT

Asphaltene is considering to be the most problematic part of oil that causes pipe plugging, permeability reduction, and ultimately loss of production through its separation process from oil body as result of any thermodynamic change. Its weight fraction is one of key inputs to any asphaltene related modeling, which requires an expensive and time consuming experimental procedure. In this research, for the first time a novel MLP-ANN structure is proposed to predict this critical parameter at wide range of operational conditions, with satisfactory precision. A dataset of over 300 experimental data was gathered from open literature and used to train and test the proposed construct. The results show network great performance and therefore, can be used as a universal tool to provide input for any asphaltene-related modeling, with assurance.     

油井中胶质沥青质沉积结垢的防治

对配制的不同配方的解堵剂,通过各种性能评价,包括腐蚀实验和破乳剂的配伍性实验,得到该解堵剂的性能较好,对油井管柱、设备均不会造成腐蚀伤害,并且不会影响原油的后续处理加工。不同配方的解堵剂不仅对塔河油田油井中沥青质堵塞物的产生有较好的抑制作用,而且对已经产生的堵塞物有较好的溶解效果,对效果好的解堵剂进行复配后发现解堵效果有明显改善;另外该解堵剂对原油还有一定的降粘作用,有利于原油的开采输送。     

Experimental study on asphaltene precipitation induced by CO2 flooding

The effects of CO2 pressure,temperature and concentration on asphaltene precipitation induced by CO2 were studied using a high-pressure vessel,interfacial tensiometer,Fourier transform infrared （FTIR） and drill core displacement experimental apparatus.The results indicated that the content of asphaltene in crude oil decreased,and the interfacial tension between a model oil and distilled water increased,with an increase of CO2 pressure,decrease of temperature and increase of molar ratio of CO2 to crude oil when CO2 contacted crude oil in the high pressure vessel.The content of asphaltene in sweepout oil and the permeability of test cores both also decreased with an increase of CO2 flooding pressure.The main reason for changes in content of asphaltene in crude oil,in interfacial tension between model oil and distilled water and in the permeability of the test core is the precipitation of asphaltene which is an interfacially active substance in crude oil.Precipitation of asphaltene also blocks pores in the drill core which decreases the permeability.     

Mechanism of the reversibility of asphaltene precipitation in crude oil

Asphaltene precipitation and deposition occur in petroleum reservoirs as a change in pressure, temperature and liquid phase composition and reduce the oil recovery considerably. In addition to these, asphaltene precipitates may deposit in the pore spaces of reservoir rock and form plugging, which is referred to as a type of formation damage, i.e. permeability reduction. In all cases above, it is of great importance to know under which conditions the asphaltenes precipitate and to what extent precipitated asphaltenes can be re-dissolved. In other words, to what extent the process of asphaltene precipitation is reversible with respect to change in thermodynamic conditions. In present work, a series of experiments was designed and carried out to quantitatively distinguish the reversibility of asphaltene precipitation upon the change in pressure, temperature and liquid composition. Experiments were conducted in non-porous media. Generally it was observed that the asphaltene precipitation is a partial reversible process for oil under study upon temperature change with hysteresis. However, the precipitation of asphaltene as a function of mixture composition and pressure is nearly reversible with a little hysteresis.     

Application of ANFIS-GA algorithm for forecasting oil flocculated asphaltene weight percentage in different operation conditions

Asphaltene which cover range of 1% to over 10% of oil by weight, is well-known as most problematic part of oil that can deposit during production in reservoir, well tubing, and surface production lines, and consequently impose a serious restriction on production which in turn increases total cost of entire operation. Through decades an extensive research has been performed in order to identify asphaltene molecular structure, its behavior at different condition, and its separation mechanism from oil. One of most critical parameter associated with asphaltene precipitation modeling is flocculated asphaltene weight percentage in oil at given operation condition. In this study, to eliminate cost and time associated with experimental procedure that concern with determining this critical parameter, a novel ANFIS network with the help of Genetic algorithm has been developed, which trained and tested by over 400 experimental data. The constructed network show good performance regarding this critical-parameter forecasting, and therefore can be used as a general tool in order to provide input for any asphaltene-concern modeling, with confidence.     

A Novel ANFIS-PSO Network for forecasting oil flocculated asphaltene weight percentage at wide range of operation conditions

Asphaltene precipitation is known as one of the challenging problems in petroleum industries which have significant effects on production such as formation damage and wellbore plugging, that consequently impose a serious restriction on production and in turn increases total cost of entire operation. Through decades an extensive research has been performed in order to identify asphaltene molecular structure, its behavior at different condition, and its departure mechanism from oil. One of most critical parameter associated with asphaltene precipitation modeling is flocculated asphaltene weight percentage in oil at given operation condition. In this investigation, to eliminate cost and time related with experimental procedure that concern with determining this critical parameter, a novel hybrid structure of ANN and FIS with the help of Genetic algorithm has been developed, which trained and tested by over 350 experimental data. The constructed network show good performance regarding flocculated weight percentage forecasting, and therefore can be used as a universal tool in order to provide input for any asphaltene-related modeling, with assurance.     

Application of novel ANFIS-PSO approach to predict asphaltene precipitation

Asphaltene precipitation is known as one of the challenging problems in petroleum industries which have significant effects on production such as formation damage and wellbore plugging. To solve this problem, calculation of precipitated asphaltene becomes highlighted so in the present study a novel approach is proposed based on ANFIS algorithm to estimate precipitated asphaltene in terms of dilution ration, carbon number of precipitants and temperature. The particle swarm optimization (PSO) method is applied to optimize ANFIS algorithm parameters. The proposed model was evaluated based on statistical parameters and the calculated R², AARD and RMSE for the total data are 0.90309, 9.4908 and 7.9468. They showed the predicting algorithm performed in acceptable manner so a high accurate and simple tool was proposed to predict the precipitated asphaltene as function of Dilution ration, temperature and carbon number of precipitants.     

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.     

Modelling asphaltene precipitation with solvent injection using cubic-PR solid model

Cubic equation-of-state solid models are commonly-used to predict asphaltene precipitation behavior. Thermodynamic parameters are needed to model this behavior under different pressures and temperatures, and are usually obtained through fitting the model to multi asphaltene onset experiments. This paper introduces an empirical linear relation (tested on six oil samples) relating Asphaltene Onset Pressure (AOP) with injected solvent amount. In addition, waxes and aromatics correlations are utilized to obtain the thermodynamic parameters within the model. The two modifications decrease the number of tuning parameters of the model, as well as reduce the number of lab measurements needed to apply it. The model is tested on two oil samples, with previously published data, to predict AOPs. Using aromatics correlations provided more rational trends for AOP than waxes correlations. Besides, both correlations create a practical domain inside which the laboratory AOP values lie. The new additions enhance the prediction capabilities of the model in the lack of asphaltene experiments.     

The use of nonylphenol formaldehyde resins for preventing asphaltene precipitation in vacuum residues and hydroprocessed petroleum samples

This work focused on the synthesis and characterization of nonylphenol formaldehyde resins (NPFR) as examples of active molecules for preventing asphaltene precipitation in vacuum residue (VR) and hydroprocessed petroleum samples. The evaluation for the NPFR as asphaltene dispersants was carried out using the on-column filtration technique at room temperature and near process conditions (195°C). The results indicated that NPFR (molecular weight = 900–4800 Da) are active for the reduction of asphaltene content of gravimetrically separated asphaltene solutions and for VR and hydroprocessed samples at room temperature (35°C) and at 195°C. It was found that the activity of NPFR as asphaltene dispersants depends not only on the type of sample (asphaltenes, virgin or processed) but also on the temperature, molecular weight, and concentration.     

Study of asphaltene deposition from Tahe crude oil

Borehole blockage caused by asphaltene deposition is a problem in crude oil production in the Tahe Oilfield, Xinjiang, China. This study has investigated the influences of crude oil compositions, temperature and pressure on asphaltene deposition. The asphaltene deposition trend of crude oil was studied by saturates, aromatics, resins and asphaltenes (SARA) method, and the turbidity method was applied for the first time to determine the onset of asphaltene flocculation. The results showed that the asphaltene deposition trend of crude oil by the turbidity method was in accordance with that by the SARA method. The asphaltene solubility in crude oil decreased with decreasing temperature and the amount of asphaltene deposits of T739 crude oil (from well T739, Tahe Oilfield) had a maximum value at 60 o C. From the PVT results, the bubble point pressure of TH10403CX crude oil (from well TH10403CX, Tahe Oilfield) at different temperatures can be obtained and the depth at which the maximum asphaltene flocculation would occur in boreholes can be calculated. The crude oil PVT results showed that at 50 , 90 and 130 o C, the bubble point pressure of TH10403CX crude oil was 25.2, 26.4 and 27.0 MPa, respectively. The depth of injecting asphaltene deposition inhibitors for TH10403CX was determined to be 2,700 m.     

Prediction of asphaltene deposition during turbulent flow using heat transfer approach

In this study, asphaltene deposition from crude oil has been studied experimentally using a test loop and prediction using theoretical study under turbulent flow (Reynolds numbers below 5000). The effects of many parameters such as oil velocity, surface temperature and concentration of flocculated asphaltene on the asphaltene deposition were investigated. The results showed that asphaltene deposition thickness increases with increasing both surface temperature and concentration of flocculated asphaltene and decreasing oil velocity. Thermal approach was used to describe the mechanisms involved in this process and the results of data fitting showed that there are good agreements between the results of the proposed model and the measured asphaltene deposition rates.     

Experimental and modeling study of asphaltene adsorption onto the reservoir rocks

Abstract

In this work, amount of asphaltene adsorption onto the carbonate and sandstone rock samples was investigated at various initial concentrations of asphaltene in oil. Asphaltene adsorption onto both types of the reservoir rocks was increased by increasing the initial concentration of asphaltene. The amount of asphaltene adsorption onto the rock samples was predicted using Langmuir and Freundlich isotherm models. The results showed that Langmuir model had a better accuracy for prediction of asphaltene adsorption onto the rock samples than Freundlich model. Furthermore, asphaltene adsorption onto the reservoir rocks was studied in the presence of a recently developed asphaltene inhibitor. The inhibitor significantly reduced asphaltene adsorption at any initial concatenation of asphaltene. Moreover, changes in the rock permeability due to asphaltene precipitation were determined in the presence and absence of the asphaltene inhibitor.     

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.     

Phase behavior modelling of asphaltene precipitation utilizing MLP-ANN approach

Since the sedimentation of heavy hydrocarbons such as asphaltenes, is the highlighted concern in production and operational, many studies were focused on this challenge in the petroleum industry. Therefore, the petroleum engineers should access to the asphaltene precipitation as an essential factor in order to conquer its problems. In this study, an empirical model for prediction asphaltene precipitation by multi-layer perceptron artificial neural network (MLP-ANN) is offered that takes the effect of the temperature, dilution ratio, and molecular weight for different n-alkanes. The output of this model showed 0.9999 for correlation coefficient (R²) and 0.000495 for mean squared error (MSE). This value illustrates the high quality of this model in compare of other available models. So far, MLP-ANN can offer significant accuracy in predicting asphaltene precipitation of asphaltene and other heavy oil.