다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Nanofluids are potential heat transfer fluids with enhanced thermophysical properties and heat transfer performance can be applied in many devices for better performances (i.e. energy, heat transfer and other performances). In this paper, a comprehensive literature on the applications and challenges of nanofluids have been compiled and reviewed. Latest up to date literatures on the applications and challenges in terms of PhD and Master thesis, journal articles, conference proceedings, reports and web materials have been reviewed and reported. Recent researches have indicated that substitution of conventional coolants by nanofluids appears promising. Specific application of nanofluids in engine cooling, solar water heating, cooling of electronics, cooling of transformer oil, improving diesel generator efficiency, cooling of heat exchanging devices, improving heat transfer efficiency of chillers, domestic refrigerator-freezers, cooling in machining, in nuclear reactor and defense and space have been reviewed and presented. Authors also critically analyzed some of the applications and identified research gaps for further research. Moreover, challenges and future directions of applications of nanofluids have been reviewed and presented in this paper. Based on results available in the literatures, it has been found nanofluids have a much higher and strongly temperature-dependent thermal conductivity at very low particle concentrations than conventional fluids. This can be considered as one of the key parameters for enhanced performances for many of the applications of nanofluids. Because of its superior thermal performances, latest up to date literatures on this property have been summarized and presented in this paper as well. However, few barriers and challenges that have been identified in this review must be addressed carefully before it can be fully implemented in the industrial applications.

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A review on applications and challenges of nanofluids

The validity of the cubic law for laminar flow of fluids through open fractures consisting of parallel planar plates has been established by others over a wide range of conditions with apertures ranging down to a minimum of 0.2 µm. The law may be given in simplified form by Q/Δh = C(2b)3, where Q is the flow rate, Δh is the difference in hydraulic head, C is a constant that depends on the flow geometry and fluid properties, and 2b is the fracture aperture. The validity of this law for flow in a closed fracture where the surfaces are in contact and the aperture is being decreased under stress has been investigated at room temperature by using homogeneous samples of granite, basalt, and marble. Tension fractures were artificially induced, and the laboratory setup used radial as well as straight flow geometries. Apertures ranged from 250 down to 4µm, which was the minimum size that could be attained under a normal stress of 20 MPa. The cubic law was found to be valid whether the fracture surfaces were held open or were being closed under stress, and the results are not dependent on rock type. Permeability was uniquely defined by fracture aperture and was independent of the stress history used in these investigations. The effects of deviations from the ideal parallel plate concept only cause an apparent reduction in flow and may be incorporated into the cubic law by replacing C by C/ƒ. The factor ƒ varied from 1.04 to 1.65 in these investigations. The model of a fracture that is being closed under normal stress is visualized as being controlled by the strength of the asperities that are in contact. These contact areas are able to withstand significant stresses while maintaining space for fluids to continue to flow as the fracture aperture decreases. The controlling factor is the magnitude of the aperture, and since flow depends on (2b)3, a slight change in aperture evidently can easily dominate any other change in the geometry of the flow field. Thus one does not see any noticeable shift in the correlations of our experimental results in passing from a condition where the fracture surfaces were held open to one where the surfaces were being closed under stress.

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VALIDITY OF CUBIC LAW FOR FLUID FLOW IN A DEFORMABLE ROCK FRACTURE - eScholarship


 The objective of infill well placement optimization is to determine the optimal well locations that maximize the net present value (NPV). The most common method of well infilling in oil field is based on the engineer's knowledge, which is risky. Additionally, numerous optimization techniques have been proposed to address the issues. However, locating the global optimum in a large-scale practical reservoir model is computationally expensive, even more so in the realistic extra-low permeability reservoir, where fractures are generated and underground conditions are complex. Thus, both determining well locations solely through human experience and obtaining them through traditional optimization methods have disadvantages in actual engineering applications. In this paper, we propose an infill well optimization strategy based on the divide-and-conquer principle that divides the large-scale realistic reservoir model into several types of small-scale conceptual models using human knowledge and then uses the surrogate-assisted evolutionary algorithm to obtain the infill well laws for this reservoir. The diamond inversed nine-spot and rectangle thinning well patterns are studied and summarized to provide the optimal infill well placement laws for extra-low permeability reservoirs. Additionally, the laws are implemented in W-77 actual reservoir and the oil recovery has an equivalent increase of 2.205%. The results demonstrate the proposed method's strong engineering potential and application value, as it combines the benefits of human experience and evolutionary algorithms to determine the optimal infill well placement in a realistic extra-low permeability reservoir development scenario.

An efficient infill well placement optimization approach for extra-low permeability reservoir

Waterflooding reservoir interwell connectivity characterization is the fundamental work in oil development, aiming to inverse the vital connecting channels between injectors and producers. In this paper, we endow an artificial neural network (ANN) with strong interpretability through the ordinary differential equation (ODE) of the material balance equation, proposing a physical knowledge fusion neural network (PKFNN). In addition, the proposed model could inherit the knowledge learned from different injector–producer pairs, fully improving the training efficiency. In this way, PKFNN combines the merits of both physical and machine learning approaches. Firstly, based on the physical control law and the ODE of the material balance equation, we endow the model with highly transparent modular architectures in the framework of feedforward neural network. In this way, our work has both high interpretability and excellent approximation ability, combining the merits of the physical and machine learning approaches. The proposed model shows great performance on productivity forecast and interwell connectivity reflection in several reservoir experiments. PKFNN provides a novel way to enhance the interpretability and robustness of the data-driven-based interwell connectivity-analyzing models by integrating the physical knowledge of waterflooding reservoirs.

/pdf/waterflooding-interwell-connectivity-characterization-and-g0x6p9kd.pdf

Waterflooding Interwell Connectivity Characterization and Productivity Forecast with Physical Knowledge Fusion and Model Structure Transfer

Complex fractures and pore structures in the rock strongly influence the mechanical properties, and the process from compression to failure is complicated. Under the action of rock stress, pore structure deformation and fractures close or propagate, easily leading to deterioration in the rock mechanical properties until rock failure. Thus, the effects of microscale factors are critical in mechanical properties such as rock strength, elastic modulus, and stress–strain state under the triaxial stress state. It is difficult for physical and mechanical experiments to obtain the qualitative rules of regular structures, but numerical simulation can make up for this defect. In this work, the accuracy of the model was proven through a comparison with previous experimental results. The true triaxial numerical simulation experiments were conducted on representative rocks and natural pore structures. These simulated results revealed that the pore and throat parameters will change abruptly when the particle model volumetric strain is between 0.0108 and 0.0157. When the fracture angle is between 45° and 75°, the fracture has a great influence on the peak stress. The angle between the natural fracture and the fracturing direction should be less than 45° as much as possible. Clay affects the rock strength by influencing the force chains formed by the rock skeleton. Fracturing is easier when the structural clay content is higher than 25%. It is easier to fracture in a direction parallel to the laminated clay when the clay content is below 27%. This work indicates the effects of rock particles, fractures, and clay on the mechanical parameters, providing key fundamental data for further quantifying the fracturing patterns.

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Digital Rock Mechanical Properties by Simulation of True Triaxial Test: Impact of Microscale Factors

History matching is an important part of reservoir numerical simulation research and the basis for predicting oilfield development performance. At present, integrated smoother and ensemble Kalman filter methods are the mainstream methods of history matching. However, these data assimilation algorithms need to be based on Gaussian assumptions and cannot guarantee the geological authenticity of the modified model. Therefore, to achieve end-to-end processing of dynamic data to modify the reservoir model, while ensuring that the distribution of data does not need to be restricted by Gaussian distribution, this paper proposes the use of the deep conditional generative adversarial network (CGAN) method for automatic reservoir history matching for the first time. The mapping relationship between static geological reservoir parameters and dynamic production data is found through the training generative network, which reduces the uncertainty of the reservoir model obtained by automatic history matching and explains parallel offline processing. The numerical simulation results show that the prediction results on the 2D two-phase flow model are consistent with the results of the proposed deep conditional generative adversarial network method.

Kai Zhang

Papers

An efficient infill well placement optimization approach for extra-low permeability reservoir

Waterflooding Interwell Connectivity Characterization and Productivity Forecast with Physical Knowledge Fusion and Model Structure Transfer

Digital Rock Mechanical Properties by Simulation of True Triaxial Test: Impact of Microscale Factors

Deep Conditional Generative Adversarial Network for History Matching with Complex Geologies

Diagnosis of pumping machine working conditions based on transfer learning and ViT model