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CO 2 -enhanced oil recovery (CO 2 -EOR) is a technique for commercially producing oil from depleted reservoirs with associated benefits of capturing and storing large quantities of CO 2 . Minimum miscibility pressure (MMP) for gas and oil system is one of the most important factors affecting projecting and developing of EOR CO 2 miscible flooding to reach maximum recovery efficiency. Determination of MMP can be performed with computational techniques and experimental methods. The slim tube test (STT), the pressure rising bubble apparatus (RBA), the vanishing interfacial tension (VIT), and recently, developed the sonic response method (SRM) can be considered as the petroleum industry experimental approaches for determination of the MMP for hydrocarbons and CO 2 . This paper presents a new technique, named rapid pressure increase method (RPI) to measure MMP and investigate the phase behavior of the analyzed fluids. The main principle of the measurement are determination of the relation between pressure increase and reduced volume of binary system and registration of the moment at which derivative of the function is the lowest. This point corresponds to ratio change of pressure increase to reduced volume and is considered as MMP. Determined values fit well with previous work and literature data. This experimental method of MMP is simple, time-saving, and more easily to conduct than other experimental techniques of measurement MMP. [ABSTRACT FROM AUTHOR]

Huff and Puff Enhanced Oil Recovery method can be regarded as promising process to increase oil production rates from developed field. Worldwide experiences in the application for an industrial-scale of this technology has been extensively discussed for heavy oil and tight oil production, however, field unique does not guarantee success for technology transfer to different site. In this way reservoir simulation is used as a first approximation of the project efficiency. However, numerical simulation requires representative data from laboratory experiments. Furthermore, huff-and-puff should be considered as complex problem, where influences from injection rates, soaking time and production rates can not be neglected. On the other side, conducting laboratory investigations are expensive and time-consuming, therefore, these researches should provide the most valuable information. In the presented methodology, laboratory experiments were conjuncted with the numerical representation of a core sample, to generate trustworthy models which were used for the process optimization. The optimal huff-n-puff operational design was computed using a stochastic population-based particle swarm optimization (PSO) method. As a consequence of high computational cost of a single full physic numerical run, the genetic programming as a novel tool for the huff-and-puff process optimization was successfully implemented. The comparison of the optimized results between genetic programming data-drive model and the full-physic numerical run revealed the right approximation and significant computing time reduction. [ABSTRACT FROM AUTHOR]

The application of Enhanced Oil Recovery (EOR) techniques are encouraged by the current oil price and the growing global demand for oil. Optimizing oil production from current resources becomes a main strategic target for many oil producing companies around the world. Among EOR processes, polymer flooding is an attractive option in many reservoirs. The objective of polymer flooding is to control water mobility inside oil reservoirs to ensure high oil recovery factors. Several designing parameters are critical for the success of polymer flooding applications. Chemical concentration depends on geological features of reservoir and well pattern are crucial to make decisions for the reservoir management. In addition, identifying the time of polymer injection is also important in a project as well as slug size. The goal of this article is to evaluate the efficient polymer injection strategy based on global optimization algorithms. Authors investigated the design of polymer flooding process in terms of optimal concentration, slug size and initiation time employ nature inspired algorithms coupling with reservoir simulation. Numerical testing and comparative study show that proposed natural-inspired algorithms outperform widely reported in literature optimization methods in that it provides a higher quality solution. [ABSTRACT FROM AUTHOR]