Asphaltene Precipitation Modeling Using Fuzzy Tuning of Scaling Equations

A major concern in the petroleum industry is asphaltene precipitation, which has negative impacts on production costs and recovery. The scaling equation is the most popular approach for modeling asphaltene precipitated out of solution in crude oils. Due to different values assigned for involved coefficients in scaling equations, they might overestimate or underestimate in some region relative to each other. This study proposes an improved strategy for tuning scaling equations and compensating effects of overestimation and underestimation through fuzzy rules. This strategy, called fuzzy tuning of scaling equations (FTSE), has a parallel framework, which gains outputs of different scaling equations and then introduces them to a fuzzy model as inputs. The fuzzy model breaks down the problem into subspaces through fuzzy membership functions and solves each region separately using fuzzy rules. Aggregating results of each subspace produces final model's output (i.e., FTSE output). Results indicated that FTSE performed more satisfyingly compared with individual scaling equations performing alone.      

Prediction of Crude Oil Asphaltene Precipitation Using Support Vector Regression

Precipitation and deposition of asphaltene during different recovery processes is an important issue in oil industry which causes considerable increase in production cost as well as negatively impacting in production rate. In this study, support vector regression as a novel computer learning algorithm was utilized to estimate the amount asphaltene precipitation from experimental titration data. Also, the result of support vector regression modeling was compared with the artificial neural network model and the scaling equation. Results show acceptable agreement with experimental data and also more accurate prediction in comparison to artificial neural network and scaling equation.      

Sweet Corrosion Inhibition on API 5L-B Pipeline Steel

Corrosion inhibition and adsorption behavior of two triazole derivatives on API 5L-B carbon steel in CO₂-saturated 3.5% NaCl solutions was investigated using potentiodynamic polarization, EIS and EFM techniques. Specimen surfaces were characterized using SEM, EDX and XRD. Results show that the two compounds are mixed-type inhibitors and inhibition efficiency increases with increasing concentrations. Adsorption of the two compounds is a mixed between chemisorptions and physisorption and obeys Langmuir adsorption isotherm. Activation energy and thermodynamic parameters were calculated. Surface analyses confirm the formation of iron nitrides on the metal surface, which supports results obtained from previous techniques.