多层构造油藏纵向细分计算单元对储量参数及结果的影响

为满足油田中后期精细开发及后续调整挖潜的需要，亟需进行更精细的储量研究，其中最重要的一环就是纵向细分计算单元.对于多层构造油藏，纵向细分主要根据油层组内隔夹层分布特征、小层物性特征，细分到以小层或者分布特征和物性相近且纵向上连续分布的小层组合为计算单元.根据纵向细分计算单元前后储量参数选值的理论推导，结合储层的沉积展布特征，认为含油面积范围内无储层尖灭时，仅平面油层厚度差异较大的油藏平均有效厚度变小.含油面积范围内储层尖灭时，正常三角洲沉积储层的油藏各参数一般都变小；而辫状河三角洲沉积储层的油藏一般平均有效厚度变小，平均有效孔隙度和平均含油饱和度变大.这一结论可以有效指导储量评估过程中纵向细分计算单元方案划分，为同类油田的精细储量研究提供技术支撑.     

Leveraging parallel spatio-temporal computing for crime analysis in large datasets: analyzing trends in near-repeat phenomenon of crime in cities

ABSTRACT

Crime often clusters in space and time. Near-repeat patterns improve understanding of crime communicability and their space–time interactions. Near-repeat analysis requires extensive computing resources for the assessment of statistical significance of space–time interactions. A computationally intensive Monte Carlo simulation-based approach is used to evaluate the statistical significance of the space-time patterns underlying near-repeat events. Currently available software for identifying near-repeat patterns is not scalable for large crime datasets. In this paper, we show how parallel spatial programming can help to leverage spatio-temporal simulation-based analysis in large datasets. A parallel near-repeat calculator was developed and a set of experiments were conducted to compare the newly developed software with an existing implementation, assess the performance gain due to parallel computation, test the scalability of the software to handle large crime datasets and assess the utility of the new software for real-world crime data analysis. Our experimental results suggest that, efficiently designed parallel algorithms that leverage high-performance computing along with performance optimization techniques could be used to develop software that are scalable with large datasets and could provide solutions for computationally intensive statistical simulation-based approaches in crime analysis.     

Parallelization of the flow-path network model using a particle-set strategy

High-performance simulation of flow dynamics remains a major challenge in the use of physical-based, fully distributed hydrologic models. Parallel computing has been widely used to overcome efficiency limitation by partitioning a basin into sub-basins and executing calculations among multiple processors. However, existing partition-based parallelization strategies are still hampered by the dependency between inter-connected sub-basins. This study proposed a particle-set strategy to parallelize the flow-path network (FPN) model for achieving higher performance in the simulation of flow dynamics. The FPN model replaced the hydrological calculations on sub-basins with the movements of water packages along the upstream and downstream flow paths. Unlike previous partition-based task decomposition approaches, the proposed particle-set strategy decomposes the computational workload by randomly allocating runoff particles to concurrent computing processors. Simulation experiments of the flow routing process were undertaken to validate the developed particle-set FPN model. The outcomes of hourly outlet discharges were compared with field gauged records, and up to 128 computing processors were tested to explore its speedup capability in parallel computing. The experimental results showed that the proposed framework can achieve similar prediction accuracy and parallel efficiency to that of the Triangulated Irregular Network (TIN)-based Real-Time Integrated Basin Simulator (tRIBS).     

基于GPU加速的Boussinesq类波浪传播变形数值模型

Boussinesq波浪模型是一类相位解析模型,在时域内求解需要较高的空间和时间分辨率以保证计算精度。为提高计算效率,有必要针对该类模型开展并行算法的研究。与传统的中央处理器(CPU)相比,图形处理器(GPU)有大量的运算器,可显著提高计算效率。基于统一计算设备架构CUDA C语言和图形处理器,实现了Boussinesq模型的并行运算。将本模型的计算结果同CPU数值模拟结果和解析解相比较,发现得到的结果基本一致。同时也比较了CPU端与GPU端的计算效率,结果表明,GPU数值模型的计算效率有明显提升,并且伴随数值网格的增多,提升效果更为明显。     

New formulae for capacity energy-based assessment of liquefaction triggering

Abstract

Liquefaction of loose saturated soil deposits is a hazardous type of ground failure occurring under earthquake excitations. Therefore, an accurate estimation of liquefaction potential is extremely important in geotechnical engineering. In the current study, a new model is proposed which estimates the level of strain energy needed for liquefaction initiation. A compiled database containing cyclic tests gathered from previously published works was used to develop new models to predict liquefaction potential. M5′ algorithm was used to find the best correlation between parameters. It was shown that not only the derived formulas are acceptably accurate but also they feature a very simple structure in comparison with available formulas in the literature. The proposed equations are accurate, physically sound and uncomplicated. Furthermore, safety factors were given for different levels of risk, which can be useful for engineering practice. In addition, the influence of different predictors on the liquefaction potential was evaluated and also the significance of input variables was assessed via sensitivity analysis. Finally, a new model was introduced for preliminary estimation of liquefaction potential.     

Boussinesq相位解析的海岸水动力学数学模型研究进展

由于在平衡计算效率和精度上具有优势,Boussinesq相位解析数学模型研究不断取得突破,已成为波浪和水流精细化模拟的较优解析方式,为海岸工程、环境、地质等问题提供了实用和高效的研究手段。本文对已有Boussinesq类模型的研究进行了评述,深入探讨其重要发展、实际应用和理论瓶颈,从高阶非静压修正、GPU准三维高性能算法编译、波浪破碎和泥沙运移沉积等4个方面提出未来可能的科学突破方向。     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

结合云计算与大数据的出租房消防安全监管GIS

我国城镇化进程和经济发展效果卓著，但消防建设规划与城市扩张发展之间并不协调。虽然现有研究已经运用GIS技术到消防安全监管当中，但依然存在信息联动性不强，可视化程度低的问题。为此，本文基于地理信息系统技术对城市开发区内地理信息数据、多源多时态遥感及航空影像、巡查数据、出租房数据等进行有效整合完善，以建立时空地理信息大数据。笔者利用虚拟化、云计算等技术建立出租房消防安全动态监管平台。此平台为管理部门提供了基于地理信息的决策支持，提升了出租房管理的高效性和科学性。系统的建成和应用对我国的出租房管理信息化工作的开展具有重要意义。     

Improved algorithms for generalized finite element simulations of three-dimensional hydraulic fracture propagation

This paper reports improvements to algorithms for the simulation of 3-D hydraulic fracturing with the Generalized Finite Element Method (GFEM). Three optimizations are presented and analyzed. First, an improved initial guess based on solving a 3-D elastic problem with the pressure from the previous step is shown to decrease the number of Newton iterations and increase robustness. Second, an improved methodology to find the time step that leads to fracture propagation is proposed and shown to decrease significantly the number of iterations. Third, reduced computational cost is observed by properly recycling the linear part of the coupled stiffness matrix. Two representative examples are used to analyze these improvements. Additionally, a methodology to include the leak-off term is presented and verified against asymptotic analytical solutions. Conservation of mass is shown to be well satisfied in all examples.     

大面积硅微条探测器在轨数据压缩算法的设计与实现

硅微条探测器具有位置分辨高、响应快、低噪声、低功耗等优点,广泛应用在各大加速器试验中,测量粒子径迹.新世纪以来,逐渐应用于空间探测领域.计划中的"悟空"2号暗物质粒子探测卫星的硅微条探测器将至数十万计,将产生海量的原始数据.如何实现探测器快速实时的数据压缩,是其需要解决的一大难题.立足于面向空间应用的硅微条探测器在轨实时压缩算法,算法采用FPGA (Field-programmable Gate Array)搭建流水线结构的方式实现,在提高系统集成度、节省逻辑资源的同时,批量数据处理时最高可将数据压缩率提升至38.4 M通道/s.算法结构具有通用性,设计思想和方案将为"悟空"2号的径迹探测器的研制提供参考.