Implementing a PSO-ANFIS model for prediction of viscosity of mixed oils

This work presents a reliable model named adaptive neuro-fuzzy inference system (ANFIS) optimized by coupled simulated annealing (PSO-ANFIS) for the prediction of viscosity of mixed oils. The model was developed based on various three and two oil sample mixture data gathered from literature covering wide range of influencing parameters. Different graphical and statistical approaches were used to evaluate the model performance in estimations of actual data. Predictions of the developed model were also compared with four well-known literature models for prediction of viscosity of mixed oils. Results show that the developed particle swarm optimization ANFIS model exhibits accurate predictions and presents reliable results compared to other literature models. The model exhibits an overall AARD% value of 2.59%.     

Gas holdup in two phase gas–liquid and three phase gas–liquid–solid production and transportation

The gas holdup is an important parameter that is needed for design and development of surface facilities and transportation pipelines in the field of petroleum engineering. There is no general model for prediction of this parameter in different systems and under different conditions. As a result, development of accurate and general models for prediction of this parameter in various situations is of great importance. This study presents new experimental gas holdup data in the kerosene+CO₂ and kerosene+N₂ systems. The experimental data were measured by using a bubble column setup. Moreover, a computer-based model namely PSO-ANFIS model is also developed for prediction of the gas holdup in different systems. A total of 818 experimental gas holdup data in various systems were utilized including the newly measured experimental data in the present work as well as experimental data from several published works in the literature. Results showed that the developed PSO-ANFIS model is accurate for prediction of experimental data with an R² value of 0.998 and average absolute relative deviation (AARD%) of 3.4%.     

Application of fuzzy c-means algorithm as a novel method to predict density of mixtures of Athabasca bitumen and heavy n-alkane

The significant number of oil reservoir are bitumen and heavy oil. One of the approaches to enhance oil recovery of these types of reservoir is dilution of reservoir oil by injection of a solvent such as tetradecane into the reservoirs to modify viscosity and density of reservoir fluids. In this investigation, an effective and robust estimating algorithm based on fuzzy c-means (FCM) algorithm was developed to predict density of mixtures of Athabasca bitumen and heavy n-alkane as function of temperature, pressure and weight percent of the solvent. The model outputs were compared to experimental data from literature in different conditions. The coefficients of determination for training and testing datasets are 0.9989 and 0.9988. The comparisons showed that the proposed model can be an applicable tool for predicting density of mixtures of bitumen and heavy n-alkane.     

Modeling of viscosity for mixtures of Athabasca bitumen and heavy n-alkane with LSSVM algorithm

The resources of heavy oil and bitumen are more than those of conventional light crude oil in the world. Diluting the bitumen with liquid solvent can decrease viscosity and increase the empty space between molecules. Tetradecane is a candidate as liquid solvent to dilute the bitumen. Owning to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of aforementioned mixture is important to decrease uncertainty. The aim of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on temperature, pressure, and weight percentage of n-tetradecane using the least square support vector machine. This computational model was compared with the previous developed correlation and its accuracy was confirmed. The value of R² and MSE obtained 1.00 and 1.02 for this model, respectively. This developed predictive tool can be applied as an accurate estimation for any mixture of heavy oil with liquid solvent.     

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.     

Prediction of density and viscosity of bitumen

Abstract

It is well known fact that temperature and pressure significantly affects density and viscosity of bitumen. The present work utilizes Gene Expression Programming (GEP) approach to develop models to predict density and viscosity of bitumen. To evaluate the accuracy of proposed GEP based models, results reported by various researchers were utilized. This includes test results regarding Athabasca, Cold Lake and Gas free bitumen. The developed GEP based models were compared with the conventional empirical regression equations. The statistical analysis indicates that GEP based models work better than other existing models for density and viscosity of bitumen.     

Application of Grid partitioning based Fuzzy inference system and ANFIS as novel approach for modeling of Athabasca bitumen and tetradecane mixture viscosity

The heavy oil and bitumen reservoirs have effective role on supplying energy due to their availability in the world. The bitumen has extremely high viscosity so this type of reservoirs has numerous problems in production and trans- portation.one of the common approach for reduction of viscosity is injection of solvents such as tetradecane. In the present study the Grid partitioning based Fuzzy inference system was coupled with ANFIS to propose a novel algorithm for prediction of bitumen and tetradecane mixture viscosity in terms of pressure, temperature and weight fraction of the tetradecane. In the present study, the coefficients of determination for training and testing phases are determined as 0.9819 and 0.9525 respectively and the models are visualized and compared with experimental data in literature. According to the results the predicting method has acceptable accuracy for prediction of bitumen and tetradecane mixture viscosity.     

PSO-ANFIS modeling of viscosity for mixtures of Athabasca bitumen and a high-boiling n-alkane

The bitumen and heavy oil reservoirs are more in number than light crude oil reservoirs in the world. To increase the empty space between molecules and decrease viscosity, the bitumen was diluted with a liquid solvent such as tetradecane. Due to the sensitivity of enhanced oil recovery process, the accurate approximation for the viscosity of mentioned mixture is important. The purpose of this study was to develop an effective relation between the viscosity of Athabasca bitumen and heavy n-alkane mixtures based on pressure, temperature, and the weight percentage of n-tetradecane using the adaptive neuro-fuzzy inference system method. For this model, the value of MRE and R² was obtained as 0.34% and 1.00, respectively; so this model can be applied as an accurate approximation for any mixture of heavy oil with a liquid solvent.     

Further model development for prediction of viscosity of mixed oils

In the present study a new model named adaptive neuron fuzzy inference system optimized by hybrid method (Hybrid-ANFIS) is developed for estimation of viscosity of mixed oils. The experimental data for development of the new model were gathered from literature. Various methods were taken into consideration to examine the accuracy and precision of the model. The outcomes of the developed model were also put into comparison with four well-known literature models. Results show that the Hybrid-ANFIS model provides acceptable and precise estimations and more satisfactory predictions in comparison with literature models. The model exhibits an overall AARD% value of 2.19%.     

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.