Proposing a novel and accurate model for forecasting dynamic viscosity of n-alkane in operational conditions

Abstract

The viscosity of fluid is known as resistivity of fluid to flow and straightly affected by temperature and pressure. As it is obvious, the viscosity of reservoir fluid is known as one of the critical parameters which extensively effect on production. Therefore, in the present paper, multilayer perceptron artificial neural network (MLP-ANN) is used as a novel and accurate model to predict dynamic viscosity of normal alkanes in the operational conditions. To this end, 228 dynamic viscosity points as function of carbon number of n-alkane, temperature, and pressure were collected from a reliable paper. The comparison between MLP-ANN outputs and experimental dynamic viscosities is performed in graphical and statistical manners. The calculated coefficients of determination 0.99739 and 0.99051 for training and testing phases express the great ability of MLP-ANN algorithm in prediction of dynamic viscosity of n-alkane. According to the analysis, MLP-ANN has enough accuracy and potential to be used as software for which applicable in petroleum industry.     

Applying MLP-ANN as a novel and accurate method to estimate gas density

In the gas industries, to increase the degree of accuracy of calculation and estimation in different processes, the importance of accurate prediction of gas properties is highlighted. The gas density, as one of the key properties in gas engineering, has a major effect in calculations. So, in the present paper, multi-layer perceptron artificial neural network (MLP-ANN) was used to predict the gas density based on molecular weight, critical pressure and critical temperature of gas, pressure, and temperature. To this end, a total number of 1240 reliable data of gas density were gathered from literature for the training and testing phases. The MLP-ANN outputs were compared with the actual data in different manners, such as statistical and graphical analyses. The coefficient of determination (R²), average absolute relative deviation (AARD), and root mean squared error (RMSE) for overall process were calculated as 1, 0.0088444, and 0.0259, respectively. The determined parameters and graphical analysis showed that the MLP-ANN has great potential and high degree of accuracy in gas density estimation.     

Predicting Bubble-point Pressure and Formation-volume Factor of Nigerian Crude Oil System for Environmental Sustainability

Abstract

This paper presents a model for predicting the bubble–point pressure (P _b ) and oil formation-volume-factor at bubble-point (B _ob ) for crude samples collected from some producing wells in the Niger-Delta region of Nigeria. The model was developed using artificial neural networks with 542 experimentally obtained Pressure-Volume-Temperature (PVT) data sets. The model accurately predicts the P _b and B _ob as functions of the solution gas-oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. In order to obtain a generalized accurate model, backpropagation with momentum for error minimization was used. The accuracy of the developed model in this study was compared with some published correlations. Apart from its accuracy, this model takes a shorter time to predict the PVT properties when compared with empirical correlations. The immediate reason for this may have to do with the non-linear nature of the empirical correlations.     

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.     

Neural computing approach for predicting vaporization enthalpy of pure hydrocarbons and petroleum fractions

The current collaboration was aimed to approximate the heat of vaporization for petroleum fractions and pure hydrocarbons through using the multi-layer perceptron artificial neural network (MLP-ANN) based on the specific gravity, molecular weight, and boiling point temperature. Furthermore, Levenberg Marquardt algorithm was utilized to train the ANN structure and optimize its tuning parameters. Another comparison was carried out between the outcomes of suggested MLP-ANN model and six well-known correlations. Better results were observed for predicting heat of vaporization by the MLP-ANN model with the obtained value of mean relative error (MRE) and R-squared (R²) equal to 1.31% and 0.9962%, respectively. This computational approach can be applied in the petroleum engineering for a precise determination of heat of vaporization.     

Prediction of Bubble Point Pressure From Composition of Black Oils Using Artificial Neural Network

In the present study, an artificial neural network (ANN) constitutive model was developed to predict bubble point pressure for the case of Canadian data. The accuracy of prediction of bubble point pressure was compared using two sets of inputs to the model. One was based on composition of the oil and the other based on easily available parameters such as solution gas-oil ratio, reservoir temperature, oil gravity, and gas relative density. The performance of bubble point pressure prediction with ANN was compared with that of equation of state (EOS) and other available empirical correlations. It was found that ANN models can produce a more accurate prediction of bubble point pressure than the existing empirical correlations and EOS calculations.     

Predicting the Bubble-Point Pressure and Formation-Volume-Factor of Worldwide Crude Oil Systems

Abstract

This paper presents models for predicting the bubble-point pressure (P _b ) and oil formation-volume-factor at bubble-point (B _ob) for crude oil samples collected from several regions around the world. The regions include major producing oil fields in North and South America, North Sea, South East Asia, Middle East, and Africa. The model was developed using artificial neural networks with 5200 experimentally obtained PVT data sets. This represents the largest data set ever collected to be used in developing P _b and B _ob models. An additional 234 PVT data sets were used to investigate the effectiveness of the neural network models to predict outputs from inputs that were not used during the training process. The network model is able to predict the bubble-point pressure and the oil formation-volume-factor as a function of the solution gas–oil ratio, the gas relative density, the oil specific gravity, and the reservoir temperature. In order to obtain a generalized accurate model, back propagation with momentum for error minimization was used. The accuracy of the models developed in this study was compared in details with several published correlations. This study shows that if artificial neural networks are successfully trained, they can be excellent reliable predictive tools to estimate crude oil properties better than available correlations. The network models can be easily incorporated into any reservoir simulators and/or production optimization software.     

Modeling the heat of vaporization of petroleum fractions and pure hydrocarbons

The present contribution was aimed to estimate the vaporization enthalpy of petroleum fractions and pure hydrocarbons by using the combination of adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) called GA-ANFIS. This tool can approximate the vaporization enthalpy as a function of the specific gravity, molecular weight, and boiling point temperature with high accuracy based on 122 data gathered from the previously published literature. Furthermore, results from the proposed model have been compared with different correlations and its acceptable predictive ability against other correlations was proved in order to the estimation of the vaporization enthalpy. The percentage of absolute relative deviation and R-squared (R²) was 1.64% and 0.9967%, respectively. This tool is simple to use and can be of considerable help for petroleum engineers to have an accurate estimation of vaporization enthalpy of hydrocarbon fractions of pure hydrocarbons.     

Application of grid partitioning based fuzzy inference system as a novel predictor to estimate dynamic viscosity of n-alkane

Abstract

The oil recovery and rate of production are highly dependent on viscosity of reservoir fluid so this term becomes one of the attractive parameters in petroleum engineering. The viscosity of fluid is highly function of composition, temperature, and pressure so in this article, Grid partitioning based Fuzzy inference system approach is utilized as novel predictor to estimate dynamic viscosity of different normal alkanes in the wide range of operational conditions. In order to comparison of model output with actual data, an experimental dataset related to dynamic viscosity of n-alkanes is gathered. The graphical and statistical comparisons between model outputs and experimental data show the high quality performance of predicting algorithm. The coefficients of determination (R²) of training and testing phases are 0.9985 and 0.9980, respectively. The mentioned statistical indexes represent the great accuracy of model in prediction of dynamic viscosity.     

采出液含水对生产气油比的影响规律

针对一些开发中后期水驱油田区块生产气油比异常升高的问题,为研究采出液含水对生产气油比的影响规律,采用从油田获取的油、气、水样品,开展了模拟地层流体高压物性测试、水驱油等一系列物理模拟实验。结果表明,在充分搅拌的条件下,模拟地层油的饱和压力和溶解气油比随水的注入而降低,且降低的幅度随含水质量分数的增加而增大。当含水质量分数为60%时,模拟地层油的饱和压力降低5.92%,溶解气油比降低9.78%,溶解气水比为2.065 cm³/g。无搅拌条件下,溶解气水比随水-油接触时间的增加而逐渐增大,注入水与模拟油静置接触约24 h,水中的溶气量达到稳定,表明注入水从注入到产出在地层运移过程中与原油接触可从油相获取气体而成为含气水。模拟采出液在地层条件下的折算溶解气油比随模拟采出液含水率的升高逐渐升高。在出口压力高于饱和压力的岩心驱替实验过程中,当含水率超过95%时,生产气油比随采出液含水率的增大而急剧升高,表明采出液高含水时,采出液含水率的变化对生产气油比具有较大的影响。     

PHYSICAL AND CHEMICAL CHARACTERIZATIONS OF ASPHALTENES FROM DIFFERENT SOURCES

ABSTRACT

Asphaltenes and resins are two of the several, but important, heavy organics present in petroleum fluids. Asphaltenes are operationally defined as the non-colatile and polar fraction of petroleum that is insoluble in n-alkanes (i.e., n-pentane). Conversely resins are defined as the non-colatile and polar fraction of petroleum that is soluble in n-alkanes (i.e., n-pentane), and aromatic solvents (i.e., toluene), and insoluble in ethyl acetate. A commonly accepted view in the petroleum chemistry is that crude oil asphaltenes form micelles which are stabilized by adsorbed resins kept in solution by aromatics. Two key parameters that control the stability of asphaltene micelles in a crude oil are the ratio of aromatics to saturates and that of resins to asphaltenes. When these ratios decrease, asphaltene micelles will coalesce and form larger aggregates. The precipitation of asphaltene aggregates can cause problems such as reservoir plugging and wettability reversal.     

Prediction of catalytic hydro conversion of normal heptane over catalysts using multi-layer perceptron artificial neural network (ANN-MLP)

Abstract

Catalytic reforming in the presence of metal-acid bifunctional catalysts is a widely used reaction in refinery industry to improve some properties of products like temperature performance of diesel and octane number of gasoline. So the ability of the prediction of Iso-C₇ selectivity during n-heptane hyroconversion is a key issue. In this study, a data set which was collected from previous publications are put in an artificial neural network-multi layer perceptron (MLP-ANN) model. Properties used as input parameters are: temperature, pressure, WHSV (weight hourly space velocity), catalysts acidity and pore volume of the catalysts, and Iso-C₇ selectivity used as the output parameter. Based on results, the MLP-ANN has great ability to estimate n-heptane hydroconversion. Root mean squared error (RMSE) and R-squared (R²) error were calculated for training, test and total set of data. For training set, test set and total set RMSE are 97915, 5.1607 and 3.9441, respectively and corresponding R² are 0.97915, 0.9334 and 0.9746 respectively.     

具有组分梯度的挥发性油藏组分分布规律

根据国内外文献报道统计了组分随深度变化的21组流体组成数据,建立了不同类型油藏流体组分梯度分布图版,初步确定挥发油系统中C₇⁺组分梯度的变化范围为0.01~0.05mol%/m;运用等温组分梯度模型预测挥发性油藏油气界面,轻质、重质摩尔组成,原油的物性参数,展示了流体组分和性质随深度的分布特征和变化规律,显示出组分最大变化率发生在油气界面附近;采用等组分和等温梯度模型预测PVT属性建立实例油藏原油体积系数、溶解气油比与油藏埋深的关系图版估计地质储量范围值。对PB油藏典型流体样品采用考虑组分梯度模拟方法重新估算的地质储量较原计算值高5.38%,溶解气原始地质储量较原计算值高3.9％。     

On prediction of asphaltene precipitation in different operational conditions utilization of LSSVM algorithm

Asphaltene which is known as one of the fractions of oil, can cause the important problems during production of crude oil in reservoir, tubing and surface facilities so these problems can influence the production cost and time. In order to predicting and solving asphaltene problems, a powerful Least squares support vector machine (LSSVM) algorithm were developed for asphaltene precipitation estimation as function of dilution ratio, temperature, precipitant carbon number, asphaltene content and API of oil. A total number of 428 measured data were utilized to train and test of LSSVM algorithm. The average absolute relative deviation (AARD), the coefficient of determination (R²) and root mean square error (RMSE) were determined as 7.7569, 0.98552 and 0.26312 respectively. Based on these statistical parameters and graphical analysis it can be concluded that the predicting algorithm has enough reliability and accuracy in prediction of asphaltene precipitation.     

Deterioration of Performance of Mixing Rules in Phase Behavior Modeling of High-Density Reservoir Fluids

Abstract

The mixing rules are used in the cubic equations of state to determine the values of the attractive force parameter, a, and the repulsive force parameter, b, mixtures. The mixing rules are applied here to reservoir fluids. It was discovered that parameter a should not be treated as a constant since it varied significantly with pressure. It was therefore regressed by two straight lines, and the resulting equation of state gave a very good fit to PVT data of reservoir fluids.     

ESTABLISHING PVT CORRELATIONS FOR OMANI OILS

ABSTRACT

An adequate knowledge of any reservoir fluid PVT properties is essential for most types of petroleum calculations. These calculations include amount of oil in the reservoir, production capacity, variations in produced gas-oil ratio during the reservoir's production life, calculation of recovery efficiency, reservoir performance, production operations and the design of production facilities. PVT properties can be measured experimentally by using collected bottom-hole or surface samples of crude oils. But, the experimental determination of PVT is time consuming and very costly. In addition, even with the availability of PVT analyses, it is often necessary to extrapolate the data to field and/or surface conditions through the use of empirical correlations. Furthermore, geological and geographical conditions are considered very critical in the development of any correlation. But, universal correlations are difficult to develop. That is why correlations for local regions, where crude properties are expected to be uniform, is a reasonable alternative. In this study, experimental PVT data for North and South Oman crudes, statistical and artificial neural network (ANN) analyses are used to develop reliable PVT correlations. Comparisons with previously published correlations are presented.     

塔里木盆地近临界油气藏相态特征

近临界流体相态变化非常复杂,易挥发油和凝析气的性质相近,常规方法难以判断其是油藏还是气藏。以塔里木盆地塔中1号气田中古43井区和金跃201井2类近临界地层流体为研究对象,通过观测近临界"乳光现象",应用气油比、相图、密度及组分等不同方法对油气藏类型进行判定,综合不同方法对比研究表明:利用测试时流体图像与无因次压力关系曲线法相结合判断近临界流体类型比较准确;塔中1号气田中古43井区属于特高含凝析油凝析气相态特征,金跃201井属于近临界挥发油相态特征。通过注气实验,近临界油气藏出现干气、凝析气、凝析液三相共存的流体特征,造成不同位置的生产井气油比差异较大。     

Applying Grid partitioning based Fuzzy inference system as novel algorithm to predict Asphaltene precipitation

Asphaltene precipitation is one of challenging problems in petroleum and chemical engineering so the importance of investigation of Asphaltene precipitation is clear. The asphaltene deposition effects on wellbore plugging, wettability alteration and facility damages. In order to solve these problems, a novel investigation based on Grid partitioning based Fuzzy inference system algorithm to predict precipitated asphaltene in terms of dilution ratio, temperature and carbon number of precipitant was developed. The predicting algorithm performance was evaluated statistically and graphically. The coefficients of determination (R²) for training and testing phases 0.9973 and 0.9900 respectively which confirm the great accuracy and high potential of predicting algorithm for estimation of precipitated asphaltene so this algorithm can be used as high accurate and simple software for prediction of asphaltene behavior in crude oil.     

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.     

An Experimental Study on the Applicability of Water-alternating-CO2 Injection in the Secondary and Tertiary Recovery in One Iranian Reservoir

Abstract

The objective of this study was to experimentally investigate the performance of water-alternating gas (WAG) injection in one of Iran's oil reservoirs that encountered a severe pressure drop in recent years. Because one of the most appropriate studies to evaluate the reservoir occurs generally on rock cores taken from the reservoir, core samples drilled out of the reservoir's rock matrix were used for alternating injection of water and gas. In the experiments, the fluid system consisted of reservoir dead oil, live oil, CO₂, and synthetic brine; the porous media were a number of carbonate cores chosen from the oilfield from which the oil samples had been taken. All coreflood experiments were conducted using live (recombined) oil at 1,700 psi and reservoir temperature of 115°F. A total of four displacement experiments were performed in the core, including two experiments on secondary WAG injection and others on the tertiary water and gas invaded zones WAG injections. Prior to each test porosity and permeability of dried cores were calculated then 100% water-saturated cores were oil-flooded to obtain connate water saturation. Therefore, all coreflooding tests started with the samples at irreducible water saturation. Parameters such as oil recovery factor, water cut, and gas-oil ratio and production pressure of the core were recorded for each test. The most similar experimental work with the main reservoir condition, indicated that approximately 64% oil were recovered after 1 pore volume of WAG process at 136,000 ppm brine salinity. Although tests show ultimate recovery of 79% and 55% for secondary and tertiary injection in gas and water invaded zones, respectively, immiscible WAG injection efficiency in the gas and water invaded zones will not be proper. In the similar test to field properties, the average pressure difference about 70 Psig was observed, which shows stable front displacement. These experiments showed that there was significant improvement in the oil recovery for alternating injection of water and CO₂, especially in the secondary recovery process. Water breakthrough time in almost all of the tests shows frontal displacement of injected fluid in cores and produced gas-oil ratio changes a little whenever the injection is miscible and increases rapidly in immiscible processes.