Asphaltene precipitation affects enhanced oil recovery processes through the mechanism of wettability alteration and blockage. Asphaltene precipitation is very sensitive to the reservoir conditions and fluid properties, such as pressure, temperature, dilution ratio, and injected fluid molecular weight. A Bayesian belief network (BBN) was used in this study as an artificial intelligence modeling tool to investigate the effect of different variables/parameters on asphaltene precipitation. The predicted results from the BBN model were compared to the experimental precipitation data obtained using high-resolution images captured in a high-pressure cell and processed by image analysis software. The cell accessories facilitate in situ visual monitoring of nuclei growth of asphaltene at high pressures and specified temperatures. The average relative absolute deviation between the model predictions and the experimental data was found to be less than 4.6%. Burst of nucleation or the onset of asphaltene precipitation was also determined at different conditions directly by the developed BBN model. A comparison between the prediction of this model and the alternatives showed that the BBN model predicts asphaltene precipitation more accurately and covers a wider range of affected variables/parameters.

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Intensive petroleum activities in the Caspian Sea lead to a considerable oil pollution in the various parts of this region. Tar balls stranded on the south coast of the Caspian Sea evidently cause (and accelerate) this serious environmental threat. In this study, a poloxamine copolymeric surfactant is used to agglomerate tar balls in a system containing the solvent, antisolvent, and Caspian Sea tar balls. This research work is conducted to determine the cumulative size distribution of tar balls in the presence of a poloxamine copolymer. The evolution of tar ball aggregates is experimentally and theoretically investigated and the steady‐state size distribution of the tar balls is estimated by Back Propagation Artificial Neural Network (ANN‐BP) and a new evolutionary algorithm, called Imperialist Competitive Algorithm (ICA). The average size of tar ball aggregates increases with time and reaches a maximum value. It then declines and eventually approaches a steady‐state condition so that an almost constant size for the particles is attained. The statistical analysis shows a good agreement between the predicted values (obtained by the neural network combined with ICA, known as ANN‐ICA) and the real data for the steady‐state size distribution of the aggregates. This research study can considerably help to further understand evolution of tar balls (that hit the beaches) and consequently to find effective and economical ways for tar balls removal from the beaches. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1901–1907, 2018

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Oil spills in water are considered as a serious environmental issue in marine areas due to considerable activities in offshore and onshore oil exploration, production and transportation over the recent years. Utilization of adsorbents appears to be an efficient way to overcome this matter.This study discusses the potential of a natural plant, called Azolla folliculoid, as a sorbent for cleanup of oil spills in seas and oceans. An experimental investigation is conducted using Azolla/water phase/ oil containment systems. The characterization of the Azolla is conducted through various techniques such as transmission electron microscopy, Brunauer−Emmett−Teller (BET) analysis and FT-IR spectroscopy. A systematic parametric sensitivity analysis is performed to comprehend the adsorption mechanism in this particular case. The results implied that the uptake capacity of Azolla in sea water is 10.2 g oil /g adsorbent for the engine oil and 5.3 g oil/g for the crude oil at T= 25 ºC and pH= 8.3. Adsorption capacity of engine oil/salty water is higher compared to other mixtures. It was also found that there is an optimal adsorption rate of the temperature of 25 o C and pH of 9. This study reveals that Azolla leaf is an efficient, economic and eco-friendly oil adsorbent for oil removal from the water surface.

Asphaltene deposition in the early stage of the oil reservoir life and later during any stimulation process emerges critical problems to the petroleum industry. Deposition of asphaltene aggregates raises strict problems in industries and demands markedly a practical and scientific knowledge of the mechanisms of aggregation and precipitation. Fluorescence emission spectroscopy has been widely used to illuminate the fundamental properties of crude oils and asphaltenes. It proposes analysis of some details of equilibrium, dynamic behavior, and aggregation composition of crude oil under specific condition. In this work, the fluorescence spectra of crude-oil extracted asphaltene samples were studied and analyzed by the application of multivariate curve resolution alternating least-squares (MCR-ALS). The asphaltene samples were extracted from crude oil of three different regions of Iran (Kuh-e-Mond, Bangestan, and Gachsaran). The excitation−emission fluorescence spectra of asphaltene solutions of variable concentrations (1.0−60.0 μg/mL) in toluene were recorded. The application of MCR-ALS analysis on the row-wise augmented matrices allows the identification of major components at different asphaltene solution concentration in each sample. The emission spectra, excitation spectra, and concentration profile of these components were deconvoluted to demonstrating structural features characteristic of each sample.

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