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Organic precipitations are highly sticky and hard if some asphaltenes are present. This causes a deposition problem to occur when thermodynamic conditions are suitable for sedimentation in a wide range of production processes. This range may start from the porous media around the oil well and continue to the production pipes. Asphaltene exists in many light and heavy oil reservoirs, which often causes problems in the process of crude oil production. Asphaltenes have up to hundreds of carbon molecules in its structure which will be precipitated as a result of natural pressure drop, temperature changes and oil composition changes.In natural depletion, the main cause of asphaltene precipitation is the reduction of pressure. Due to the process of oil production from the well, which is accompanied by simultaneous reduction of pressure and temperature, this molecule is deposited to the tube wall during three stages of precipitation, growth and deposition, and causes flow obstruction.The precipitated asphaltene generated in the process of oil production from the reservoir closes the well and transmission lines. In the process of exploitation, the chock valves, separators, and other equipment in the way are blocked and broken. In refineries and petrochemicals, the presence of even small amounts of asphaltene results in a significant drop in the efficiency of catalysts and other additives. Therefore, before the reactions are performed, attempts are made to remove as much of these materials as possible from oil.In this paper, scientific literature related to the chemical structure and thermodynamic behavior of the asphaltene molecule has been investigated in order to provide clear overviews of the asphaltene precipitation and deposition, and the processes that lead to its occurrence in the well. Then, the precipitation and deposition of asphaltene in the well column and its effective factors are investigated.

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The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock – intricately impairing its permeability and hindering the flow. Historically, the effect of ions is attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to unveil the influencing hierarchy of the parameters involved in driving the process within major rock categories – sandstone and carbonate – to regulate a target functionality. The functionalities considered evolve around maximizing the oil recovery, minimizing permeability impairment/ scale damage. A pool of experimental as well as field data was used for this sake, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species - altering under each functionality – which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of ion of a single type, in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind – suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data – evidenced by the minimal errors in the bootstrapped results. Doi: 10.28991/HIJ-2021-02-03-05 Full Text: PDF

Gas injection is the most commonly used approach in enhanced oil recovery processes in the petroleum industry. Natural gas, nitrogen, and carbon dioxide are the most popular candidates for gas injection. However, gas cap (G.C.) could be another candidate somewhere, which is beneficial and cost-efficient. Therefore, it is essential to investigate and evaluate this kind of gas in the reservoir. Nevertheless, one of the most important key parameters that play an effective role in gas injection economy is the minimum miscibility pressure (MMP). MMP could be calculated with experimental investigations, empirical correlations, and computational methods like slim tube test and vanishing interfacial tension (VIT) or by means of reservoir simulation software such as ECLIPSE. This research is aiming at finding out the MMP of the oil/G.C. system and its mechanism; therefore, it has been attempted to appraise the MMP of the live/dead oil−G.C. system along with its mechanism at a constant reservoir temperature by the VIT technique. The results showed that the extracted MMP for the live oil−G.C. system is more than that for dead oil−G.C., and this value depends upon temperature, pressure, and composition of the system.