DEPOSIT FORMATION DUE TO INSTABILITY AND CONTAMINATION OF RAW MATERIALS IN ALKALI-TREATED LIGHT DISTILLATES

Alkali treating of petroleum distillates is carried out to obtain the advantages of the product purity and to improve performance. The use and quality of alkaline solutions must be controlled since metal ions present as impurities can catalyze low temperature oxidation and polymerization of olefinic compounds; leading to formation of heavy emulsions which tend to deposit in hydrocarbon phases and eventually to block the handling systems. The effects of the variable factors “time, temperature, antioxidant and anticorrosion additives” were studied. The addition of a small amount of methanol was found to retard the deposit formation. This can be ascribed to ion-molecule reactions of methoxide ions with aryl and heavy thiols present in the light distillates. The promoting effects of light and the surrounding air on the stability of final treated product have been investigated.  

Viscosity prediction in selected Iranian light oil reservoirs: Artificial neural network versus empirical correlations

Viscosity is a parameter that plays a pivotal role in reservoir fluid estimations. Several approaches have been presented in the literature (Beal, 1946; Khan et al, 1987; Beggs and Robinson, 1975; Kartoatmodjo and Schmidt, 1994; Vasquez and Beggs, 1980; Chew and Connally, 1959; Elsharkawy and Alikhan, 1999; Labedi, 1992) for predicting the viscosity of crude oil. However, the results obtained by these methods have significant errors when compared with the experimental data. In this study a robust artificial neural network (ANN) code was developed in the MATLAB software environment to predict the viscosity of Iranian crude oils. The results obtained by the ANN and the three well-established semi-empirical equations (Khan et al, 1987; Elsharkawy and Alikhan, 1999; Labedi, 1992) were compared with the experimental data. The prediction procedure was carried out at three different regimes: at, above and below the bubble-point pressure using the PVT data of 57 samples collected from central, southern and offshore oil fields of Iran. It is confirmed that in comparison with the models previously published in literature, the ANN model has a better accuracy and performance in predicting the viscosity of Iranian crudes.  

A generalized set of correlations for plus fraction characterization

The importance of accurate determination of the critical properties of plus fractions in prediction of phase behaviour of hydrocarbon mixtures by equations of state is well known in the petroleum industry. It has been stated in various papers (Elsharkawy, 2001) that using the plus fraction as a single group in equation of state calculations reduces the accuracy of the results. However in this work it has been shown that using the proper values of critical temperature and pressure for the plus fraction group can estimate the properties of hydrocarbon mixtures, and they are accurate enough to be used in reservoir engineering and enhanced oil recovery calculations. In this paper, a new method is proposed for calculating the critical properties of plus fractions of petroleum fluids. One can use this method either in predicting critical pressure and temperature of single carbon numbers (SCNs) after the splitting process or in predicting critical pressure and temperature of the plus fraction as a single group. A comparison study is performed against Riazi-Daubert correlation (Riazi and Daubert, 1987) and Sancet correlations (Sancet, 2007) for 25 oil samples taken from 14 fields from southwest Iran. The results indicate the superiority of the proposed method to the Riazi-Daubert and Sancet correlations.  

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.  

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.  

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.  

Evaluation of enhanced oil recovery fromclay-rich sandstone formations

In the last few years, there has been a growing interest in smart water (SW) flooding as economically and environmentally friendly method to Enhanced Oil Recovery (EOR) in sandstone and carbonated reservoirs. Formation damage especially fines migration and clay swelling by lowering salinity and changing the ionic environment, causes the significant decrease in permeability of the sandstone reservoirs. In this study, an experimental study has been undertaken to illuminate the effect of formation damage during smart water injection as the function of clay types. The state of the art procedure has been established in direction of sandpack construction containing favorable clay content. Injection of smart water was performed in sandpacks with different clay types (montmorillonite and kaolinite). The results show that the presence of montmorillonite augments formation damage and enhances oil recovery. Analyzing Interfacial Tension (IFT) experimental data showed that interaction of oil/SW had no great influence on increasing oil recovery. The results have been achieved based on extensive experiments including Differential Pressure (DP) measurements, Zeta potential, and Recovery Factor (RF). Two mechanisms were proposed to interpret permeability reduction and amount of oil produced values which are clay swelling, and detachment/re-attachment for montmorillonite and kaolinite, respectively.  

A PSO-ANFIS framework for prediction of density of bitumen diluted with solvents

One of the important properties in petroleum engineering calculations in heavy oil reservoirs is the density of bitumen diluted with solvents. It is required in newly developed solvent based enhanced oil recovery methods. Hence, developing accurate models for prediction of this parameter is essential. To tackle this issue, this study presents an accurate model based on adaptive neuro-fuzzy inference system trained by particle swarm optimization (PSO-ANFIS) for estimation of density of bitumen diluted with solvents and hydrocarbon mixtures using experimental data from literature. The accuracy and reliability of results were evaluated by utilizing various statistical and graphical approaches and comparing the predictions of the developed model with literature models. The analysis showed that the PSO-ANFIS model is capable to predict the experimental data with acceptable error and high accuracy. The predictions of the PSO-ANFIS model were also better than the literature models.  

The effect of different solvents on the density of undersaturated athabasca bitumen: Application in VAPEX and ES-SAGD

Predicting the density of bitumen after solvent injection is highly required in solvent-based recovery techniques like expanding solvent-steam assisted gravity drainage (ES-SAGD) and vapor extraction (VAPEX) in order to estimate the cumulative oil recovery by these processes. Using experimental procedures for this purpose is so expensive and time-consuming; therefore, it is crucial to propose a rapid and accurate model for predicting the effect of various solvents on the dilution of bitumen. In this study, an adaptive neuro-fuzzy interference system is introduced to estimate the effect of methane, ethane, propane, butane, carbon dioxide, and n-hexane on the density of undersaturated Athabasca bitumen in wide ranges of operating conditions. The obtained results were in an excellent agreement with experimental data with coefficients of determination (R²) of 0.99997 and 0.99948 for training and testing datasets, respectively. Statistical analyses illustrate the superiority of the proposed model in predicting the bitumen density at different conditions.