Activating solution gas drive as an extra oil production mechanism after carbonated water injection

Enhanced oil recovery (EOR) methods are mostly based on different phenomena taking place at the interfaces between fluid–fluid and rock–fluid phases. Over the last decade, carbonated water injection (CWI) has been considered as one of the multi-objective EOR techniques to store CO₂ in the hydrocarbon bearing formations as well as improving oil recovery efficiency. During CWI process, as the reservoir pressure declines, the dissolved CO₂ in the oil phase evolves and gas nucleation phenomenon would occur. As a result, it can lead to oil saturation restoration and subsequently, oil displacement due to the hysteresis effect. At this condition, CO₂ would act as in-situ dissolved gas into the oil phase, and play the role of an artificial solution gas drive (SGD).In this study, the effect of SGD as an extra oil recovery mechanism after secondary and tertiary CWI (SCWI-TCWI) modes has been experimentally investigated in carbonate rocks using coreflood tests. The depressurization tests resulted in more than 25% and 18% of original oil in place (OOIP) because of the SGD after SCWI and TCWI tests, respectively. From the ultimate enhanced oil recovery point of view, the efficiency of SGD was observed to be more than one-third of that of CWI itself. Furthermore, the pressure drop data revealed that the system pressure depends more on the oil production pattern than water production.     

Vapor Liquid Equilibria for Ionic Liquid/Ethanol/Water Systems and the Effect of Anion Hydrolysis

The performance of two tetrafluoroborate-based ionic liquids (ILs) as entrainers in the dehydration of water/ethanol azeotropic mixtures was evaluated. Isobaric vapor-liquid equilibrium (VLE) data were measured for the systems ethanol/water/1-butyl-3-methyl imidazolium tetrafluoroborate and ethanol/water /n-butylpyridinium tetrafluoroborate including the azeotropic region. VLE data for the ethanol/water, ethanol/IL, and water/IL binary mixtures were obtained at 100 kPa. The hydrolysis of the tetrafluoroborate anion was studied for both types of ILs by ¹⁹F NMR analysis. The hydrolysis of the tetrafluoroborate anion does not have much effect on the ethanol/water VLE. The ¹⁹F NMR analysis indicated that hydrolysis occurred at high mole fractions of water.     

A regression modelling approach for optimizing segmentation scale parameters to extract buildings of different sizes

Multiresolution segmentation (MRS) is one of the most commonly used image segmentation algorithms in the remote-sensing community. This algorithm has three user-defined parameters: scale, shape, and compactness. The scale parameter (SP) is the most crucial one in determining the average size of the image segments generated. Since setting this parameter typically requires a trial-and-error process, automatically estimating it can expedite the segmentation process. However, most of automatic approaches are still iterative and can lead to a time-consuming process. In this article, we propose a new, non-iterative framework for estimating the SP with an emphasis on extracting individual urban buildings. The basis of the proposed method is to investigate the feasibility of associating the size of urban buildings with a corresponding ‘optimal’ (or at least reasonable) SP using an explicit mathematical equation. Using the proposed method, these two variables are related to each other by constructing a mathematical (regression) model. In this framework, the independent variables were chosen to be the typical size of buildings in a given urban area and the spatial resolution of the image under consideration; and the dependent variable was chosen to be the corresponding optimal SP. To assess the potential of the proposed approach, two regression models that yielded explicit equations (i.e. degree-2 polynomial (DP), and regression tree (RT)) were employed. In addition, as a sophisticated and versatile nonlinear model, support vector regression (SVR) was utilized to further measure the performances of DP and RT models compared with it. According to the comparisons, the DP model was selected as a representative of the proposed approach. In the end, to evaluate the proposed methodology, we also compared the results derived from the DP model with those derived from the Estimation of Scale Parameter (ESP) tool. Based on our experiments, not only did the DP model produce acceptable results, but also it outperformed ESP tool in this study for extracting individual urban buildings.     

Analysis of n-alkanes in water samples by means of headspace solvent microextraction and gas chromatography

A simple and efficient headspace solvent microextraction (HSME) was developed for the simultaneous determination of the trace concentrations of some n-alkanes in water samples. Therefore, a microdrop of an organic solvent was extruded from the needle tip of a gas chromatographic syringe to the headspace above the surface of the solution in a sealed vial. Then the volatile organic compounds are extracted and concentrated in the microdrop. Next, the microdrop was retracted into the microsyringe and injected directly into the gas chromatograph. Experimental parameters which control the performance of HSME such as the type of microextraction solvent, organic drop and sample volume, sample stirring rate, sample solution and microsyringe needle temperatures, salt addition and exposure time profiles were investigated and optimized. Finally, the enrichment factor, dynamic linear range (DLR), limit of detection (LOD) and precision of the method were evaluated. Using optimum extraction conditions, good linearity with correlation coefficients in the range of 0.995相似文献   

CMRules: Mining sequential rules common to several sequences

Sequential rule mining is an important data mining task used in a wide range of applications. However, current algorithms for discovering sequential rules common to several sequences use very restrictive definitions of sequential rules, which make them unable to recognize that similar rules can describe a same phenomenon. This can have many undesirable effects such as (1) similar rules that are rated differently, (2) rules that are not found because they are considered uninteresting when taken individually, (3) and rules that are too specific, which makes them less likely to be used for making predictions. In this paper, we address these problems by proposing a more general form of sequential rules such that items in the antecedent and in the consequent of each rule are unordered. We propose an algorithm named CMRules for mining this form of rules. The algorithm proceeds by first finding association rules to prune the search space for items that occur jointly in many sequences. Then it eliminates association rules that do not meet the minimum confidence and support thresholds according to the sequential ordering. We evaluate the performance of CMRules in three different ways. First, we provide an analysis of its time complexity. Second, we compare its performance (in terms of execution time, memory usage and scalability) with an adaptation of an algorithm from the literature that we name CMDeo. For this comparison, we use three real-life public datasets, which have different characteristics and represent three kinds of data. In many cases, results show that CMRules is faster and has a better scalability for low support thresholds than CMDeo. Lastly, we report a successful application of the algorithm in a tutoring agent.     

Relativistic modelling of a superdense star containing a charged perfect fluid

The paper presents a variety of classes of interior solutions of the Einstein-Maxwell field equations for a static, spherically symmetric distribution of a charged fluid of well-behaved nature. They describe perfect fluid balls with positive finite central pressure and density; their ratio is less than one (c = 1), and the causality condition is obeyed at the center. The outmarch of pressure, density, pressuredensity ratio and the adiabatic speed of sound is a monotonic decrease, in a physically appealing manner. A certain class of these well-behaved solutions is studied extensively. For this class, the mass of the configuration is maximized. In particular, for a surface density ?? _b = 2×10¹⁴ g/cm³ we obtain a star with a maximummass of 3.47M _??, a radius of 15.21 km and the central redshift 1.014385.     

An RL-based scheduling algorithm for video traffic in high-rate wireless personal area networks

The emerging high-rate wireless personal area network (WPAN) technology is capable of supporting high-speed and high-quality real-time multimedia applications. In particular, video streams are deemed to be a dominant traffic type, and require quality of service (QoS) support. However, in the current IEEE 802.15.3 standard for MAC (media access control) of high-rate WPANs, the implementation details of some key issues such as scheduling and QoS provisioning have not been addressed. In this paper, we first propose a Markov decision process (MDP) model for optimal scheduling for video flows in high-rate WPANs. Using this model, we also propose a scheduler that incorporates compact state space representation, function approximation, and reinforcement learning (RL). Simulation results show that our proposed RL scheduler achieves nearly optimal performance and performs better than F-SRPT, EDD + SRPT, and PAP scheduling algorithms in terms of a lower decoding failure rate.     

Information schema constructs for defining warehouse databases of genotypes and phenotypes of system manifestation features

Our long-term objective is to develop a software toolbox for pre-embodiment design of complex and heterogeneous systems, such as cyber-physical systems. The novelty of this toolbox is that it uses system manifestation features (SMFs) for transdisciplinary modeling of these systems. The main challenges of implementation of the toolbox are functional design- and language-independent computational realization of the warehouses, and systematic development and management of the various evolving implements of SMFs (genotypes, phenotypes, and instances). Therefore, an information schema construct (ISC) based approach is proposed to create the schemata of the associated warehouse databases and the above-mentioned SMF implements. ISCs logically arrange the data contents of SMFs in a set of relational tables of varying semantics. In this article we present the ISCs necessary for creation of genotypes and phenotypes. They increase the efficiency of the database development process and make the data relationships transparent. Our follow-up research focuses on the elaboration of the SMF instances based system modeling methodology.