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关键词： geological modeling   geologic modeling   reservoir characterization   enhanced recovery   artificial intelligence   reservoir simulation   fluid dynamics   exponent   core analysis   well logging  

摘要： Rock typing into flow units (FUs) plays a pivotal role in constructing static and dynamic models of petroleum reservoirs. Decisions made by asset teams mostly depend on predictions of how fluids will percolate through the subsurface during the reservoir life cycle. In carbonate settings, dealing with rock typing is complex and can generate a large quantity of units because of diagenetic processes such as dissolution, cementation, and silicification. Most rock-typing methods in carbonates successfully classify small-scale flow heterogeneity in well resolution but fail when interpolating those facies further away from the 1D domain, because of the lack of correlation between FUs and spatial data. Seismic data can be used to detect large-scale FUs and assist the interpolation of small-scale FUs in 3D reservoir volume, thus producing more-realistic static and dynamic models. We propose a modification of the classical rock-typing methods that use permeability (k) vs. porosity (/) plots and electrical properties, with a data set from the Mero Field, part of the giant Libra Field of presalt carbonate reservoirs in offshore Brazil. From the permeability cumulative S-curve analysis, we established major large-scale FUs that maintain part of the carbonate flow heterogeneity and correlate them with the elastic attributes: P-impedance (PI) and S-impedance (SI). In addition, we established a priori PI and SI correlations with the formation-factor (FF) (F) parameter to categorize large-scale FUs using the F vs. k methodology. With the large-scale FUs mapped in seismic data sets, core-plug-scale FUs can be populated into the 3D static and dynamic models using geostatistics tools, thus creating more-realistic reservoir models and assisting asset teams in the decision-making process.

关键词： Sewage sludge   Fluidized bed   Pyrolysis   Condensate   Non-condensable gas  

摘要： This paper studied influences of temperature and additives on sewage sludge (SS) pyrolysis in a fluidized bed. The maximum condensate yield (17.07 wt%) was observed with Ca-bentonite at 650 degrees C. The yields of non-condensable gases rose significantly with addition of catalysts (e.g. kaolin). Fourier transform infrared (FTIR) spectra indicated catalysts led to the appearance of some compounds (e.g. alkanes, ketones, amines) in the pyrolysis oil (PO). Gas chromatography mass spectrometry (GC-MS) revealed that PO mainly contained organo-oxygen species, nitrogen-containing heterocyclic compounds, hydrocarbons and antibiotics. With catalysts addition, the main components in the organo-oxygen species were phenols (1.59%-13.78%) and ketones (1.15%-5.12%). The highest CO2 and CO yields were obtained with kaolin addition, while the lowest CO2 yield was reached by CaO. Nitrogen (N) and chlorine (Cl) mainly existed in char and liquid phase, whereas sulfur (S) was dominated in char, indicating that the existence form of S was stable. CaO had certain N retention ability and S retention capability was up to 99.64%. Cabentonite and CaO reduced the release of Cl and all catalysts contributed to reduction of HCN emission. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： gradient-like flow   topological classification   topological conjugacy   n-sphere   linear-time algorithm  

摘要： In this paper, we study gradient-like flows without heteroclinic intersections on an n-sphere up to topological conjugacy. We prove that such a flow is completely defined by a bicolor tree corresponding to a skeleton formed by codimension one separatrices. Moreover, we show that such a tree is a complete invariant for these flows with respect to the topological equivalence also. This result implies that for these flows with the same (up to a change of coordinates) partitions into trajectories, the partitions for elements, composing isotopies connecting time-one shifts of these flows with the identity map, also coincide. This phenomenon strongly contrasts with the situation for flows with periodic orbits and connections, where one class of equivalence contains continuum classes of conjugacy. In addition, we realize every connected bicolor tree by a gradient-like flow without heteroclinic intersections on the n-sphere. In addition, we present a linear-time algorithm on the number of vertices for distinguishing these trees.

关键词： Olive stones pyrolysis   Characteristic heating time   Characteristic pyrolysis time   Distributed activation energy model (DAEM)   Lumped capacitance method (LCM)  

摘要： Pyrolysis of crushed olive stone particles in a lab scale Bubbling Fluidized Bed (BFB) reactor was investigated. The time evolution of the pyrolysis conversion degree of the olive stone particles, while moving freely in the BFB, was determined from the evolution of the mass of olive stones remaining in the bed, measured by a precision scale holding the whole reactor installation. The experimental measurements of the pyrolysis conversion degree were employed to validate a simple model combining heat transfer and chemical kinetics, which is valid for thermally small particles. The model combines the Lumped Capacitance Method (LCM) and the simplified Distributed Activation Energy Model (DAEM) to account for heat transfer and pyrolysis chemical kinetics, respectively. The estimations of the combined LCM-DAEM model for the pyrolysis conversion degree were found to be in good agreement with the experimental measurements for the pyrolysis of olive kernels in a BFB operated at various bed temperatures, fluidizing gas velocities, and biomass particle size ranges. From the combined LCM-DAEM model, the characteristic heating time and the pyrolysis time of the olive stone particles were derived, obtaining a direct relation between these two parameters for constant values of the bed temperature. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： impedance spectroscopy   impedance flow cytometry   bacteria detection   bacteria characterization   bacteria inactivation   lab-on-a-chip  

摘要： Bacteria detection, counting and analysis is of great importance in several fields. When viability plays a major role in decision making, the counting of colony-forming units grown on agar plates remains the gold standard. However, because plate counts depend on the growth of the bacteria, it is a slow procedure and only works with culturable species. Impedance flow cytometry (IFC) is a promising technology for particle detection, counting and characterization. It relies on the perturbation of an electric field by particles flowing through a microfluidic channel. The perturbation is directly related to the electrical properties of the particles, and therefore provides information about their composition and structure. In this work we investigate whether IFC can be used to differentiate viable cells from inactivated cells. Our findings demonstrate that the specific viability state of the bacteria has to be considered, but that with proper characterization thresholds, IFC can be used to classify bacterial viability states. By using three different inactivation methods-ethanol, heat and autoclavation-we have been able to show that the impedance response of Escherichia coli depends on its viability state, but that the specific response depends on the inactivation method. With these findings we expect to be able to optimize IFC for more reliable bacteria detection and counting in the future.

关键词： Dual fluidized bed   Gasification   Biomass   Segregation   Numerical simulation   Renewable energy  

摘要： In this work, computational fluid dynamics is adopted to simulate steam-biomass gasification in a dual fluidized bed reactor system. Under the Eulerian-Lagrangian multiphase framework, the large eddy simulation and multiphase particle-in-cell models are selected to track the gas and solid phases, respectively. The compositions of the gaseous products numerically obtained at the gasifier outlet are compared with the experimental data for model validation. Then, the radial and axial segregation of heat carrier with a wide particle size distribution together with the spatial variation of the microscale properties of solid phase are explored. The results show that the apparent size-induced segregation of heat carrier results in the distribution of large particles in the bottom region of both the combustor and gasifier, while the accumulation of fine particles in the freeboard of the gasifier. The mass of heat carrier distributed in the combustor is nearly 10% of that in the gasifier. The particles in the combustor have larger Reynolds number and dispersion coefficient as compared with that in the gasifier. For a specific particle size distribution, reducing the particle size enlarges the temperature, heat transfer coefficient, particle Reynolds number. Different solid residence behaviors appear in different components of the bed. A wider size distribution of heat carrier slightly enlarges the mean residence time in the combustor but obviously diminishes it in the gasifier, and also evidently reduces the solid circulation. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flow classification   Software-defined data center network   Deep learning   Deep residual learning   Gated recurrent unit  

摘要： in a data center network, accurately classifying flow is the key to optimal schedule flow. However, the existing classification methods cannot meet the demand of real networks in terms of classification performance, detection latency, and control overhead. Thus, by combining the ability of deep learning to describe the multi-dimensional features, and the advantage of software-defined networking (SDN) in centrally controlling the network from a global viewpoint, this paper proposes a fine-grained flow classification method. This paper uses random forest technology to select eight important features in three dimensions-time distribution feature of flow, real-time feature of flow and packet header feature-for the classification model. First, this paper proposes a 2-classification scheme with a two-level architecture of pre-classification and exact-classification to detect elephant/mice flows. The pre-classification model using deep residual learning + A-Softmax with cost-sensitive is deployed on a SDN switch at the network edge to filter out a large number of mice flows. The exact-classification model using deep residual learning + AM-Softmax is deployed on the SDN controller to accurately identify the elephant flows. Second, this paper proposes a 4-classification scheme based on gated recurrent unit (GRU) to detect elephant/cheetah/tortoise/porcupine flows. Finally, the experiment results show that, when the 5th packet of a flow arrives, the 2-classification scheme can achieve a recall of up to 97.31%, an accuracy of up to 93.6%, a control overhead of 0.1kbps, and a detection latency of 7 ms. At the same time, the 4-classification scheme can achieve a recall, an accuracy, a false positive rate (FPR), a Kappa, a control overhead, and a detection latency of 83.58%, 86.53%, 5.02%, 0.797, 0.61kbps and 7.5 ms on an average, respectively. Compared with the existing methods (FlowSeer, NELLY and ESCA), all performance measures are improved to different degrees. At the same ti

关键词： 铜钨矿床   成矿流体   成因机制   见龙  

摘要： 见龙铜钨矿床是位于江西兴国县境内的石英脉型矿床。矿体产于燕山期花岗质侵入体的内外接触带中,主要矿化类型为石英脉型。本研究在详细的野外地质调查和矿床地质研究基础上,对见龙铜钨矿床开展了流体包裹体研究。结果显示:见龙铜钨矿床主要经历了两期成矿流体活动,成矿流体具有中低温、低盐度特征,流体包裹体中水的δ18OH2O值为-0.96‰^-3.35‰,δDV-SMOW值为-61.6‰^-82.2‰,成矿流体主要来源于岩浆水与大气降水的混合。流体包裹体特征表明:成矿早期的流体不混熔和晚期的流体混合可能是见龙石英脉型铜钨矿床矿质沉淀的主要机制。