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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. [ABSTRACT FROM PUBLISHER]

One of problematic topics in petroleum engineering is Asphaltene precipitation issue which causes problems such as tubing plugging and formation damage due to temperature, pressure and composition changes so the notability of this issue increases. In the present investigation a novel Fuzzy c-means (FCM) algorithm was developed to predict precipitated asphaltene as function of dilution ratio, carbon number of precipitants and temperature for solving the problem. The results showed that this novel approach has great ability to predict precipitated asphaltene in terms of aforementioned parameters. The coefficients of determination (R2) for training and testing steps are calculated as 0.9828 and 0.9387 respectively. This great degree of accuracy expresses that the predicting algorithm has potential to be utilized as software for prediction of asphaltene behavior. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on handling asphaltenes deposition are still lacking. In this study, three efficient artificial intelligent models including group method of data handling (GMDH), least squares support vector machine (LSSVM), and artificial neural network (ANN) are proposed for estimating asphaltenes adsorption onto NiO/SAPO-5, NiO/ZSM-5, and NiO/AlPO-5 nanocomposites based on a databank of 252 points. Variables influencing asphaltenes adsorption include pH, temperature, amount of nanocomposites over asphaltenes initial concentration (D/C0), and nanocomposites characteristics such as BET surface area and volume of micropores. The models are also optimized using nine optimization techniques, namely coupled simulated annealing (CSA), genetic algorithm (GA), Bayesian regularization (BR), scaled conjugate gradient (SCG), ant colony optimization (ACO), Levenberg–Marquardt (LM), imperialistic competitive algorithm (ICA), conjugate gradient with Fletcher-Reeves updates (CGF), and particle swarm optimization (PSO). According to the statistical analysis, the proposed RBF-ACO and LSSVM-CSA are the most accurate approaches that can predict asphaltenes adsorption with average absolute percent relative errors of 0.892% and 0.94%, respectively. The sensitivity analysis shows that temperature has the most impact on asphaltenes adsorption from model oil solutions. [ABSTRACT FROM AUTHOR]