Prediction of volumes and risk in hydrocarbon exploration: a quantification of geology

 The prediction of the hydrocarbon potential of a specific trap or of a number of specific traps (venture), referred to herein as prospect appraisal, concerns a probabilistic exercise based on the quantification of geology in terms of structural closure, reservoir quality, hydrocarbon charge, and the retention potential of the seal. Its objectives include: (a) prediction of the hydrocarbon volumes that could be present in the trap from an analysis of its geologic attributes; (b) the amount of uncertainty introduced in the volumetric prediction by the uncertainties in the subsurface geology; (c) the risk that one or more of the essential attributes of the prospect are underdeveloped and recoverable reserves are absent. The uncertainty of the geologic input requires a probabilistic approach, for which the Monte Carlo procedure is well suited. Prospect appraisal forms the basis for decision-making in oil exploration and development and, therefore, should be reliable, consistent, and auditable. This requires the use of a consistent methodology, the development of reliable models to quantify the geologic processes involved, and the collection of comprehensive and relational databases for the many geologic variables. As a result of data availability, uncertainty and risk tend to increase strongly from mature, producing basins to areas of frontier exploration. This may complicate management of exploration portfolios. Received: 1 July 1996/Accepted: 25 November 1996     

Karst database development in Minnesota: design and data assembly

The Karst Feature Database (KFD) of Minnesota is a relational GIS-based Database Management System (DBMS). Previous karst feature datasets used inconsistent attributes to describe karst features in different areas of Minnesota. Existing metadata were modified and standardized to represent a comprehensive metadata for all the karst features in Minnesota. Microsoft Access 2000 and ArcView 3.2 were used to develop this working database. Existing county and sub-county karst feature datasets have been assembled into the KFD, which is capable of visualizing and analyzing the entire data set. By November 17 2002, 11,682 karst features were stored in the KFD of Minnesota. Data tables are stored in a Microsoft Access 2000 DBMS and linked to corresponding ArcView applications. The current KFD of Minnesota has been moved from a Windows NT server to a Windows 2000 Citrix server accessible to researchers and planners through networked interfaces.     

应用分布式存储技术优化省级CIMISS数据服务能力

随着气象数据量的不断增长,进一步提升CIMISS数据管理和服务能力的需求变得日益迫切。为解决存储系统动态扩展能力不足、并行计算与吞吐效率低下等限制CIMISS继续发展的问题,采用分布式文件系统和NAS技术替代GPFS建设共享文件系统,实现非结构化气象数据的存储功能;采用分布式数据库替代Oracle RAC建设关系数据库管理系统,实现结构化气象数据的存储功能和非结构化气象数据的索引功能。实践证明,该方案能够有效地改善CIMISS的数据存储能力、并发响应能力,适应未来气象业务对数据存储和应用的需求。     

基于持久化的网吧管理系统数据库设计和实现

设计了一种健壮持久层体系结构,并运用该结构,根据对象/关系数据库映射（O/R Mapping）策略,设计和实现了基于持久化的网吧管理系统数据库。这种实现数据持久化的方案在C＋＋开发环境下,具有比较重要的应用价值。     

GIS数据模型研究与实践

着重分析了 GIS数据建模、地理数据的 3层结构及面向对象的数据模型 ,并在此基础上提出了一种基于关系数据库的面向对象 GIS数据模型     

Risk, rescue and emergency services: The changing spatialities of Mountain Rescue Teams in England and Wales

This paper considers the role of the emergency services in society and, in particular, their role in controlling, mitigating and resolving risk. Using a network approach, Mountain Rescue Teams are studied in order to examine how people, agencies, animals, technology and knowledge are deployed to resolve emergencies. The paper traces the changing nature of risk in rural places and the impact of state regulation on the deployment, spatialities and practices of the emergency services. In doing so, it argues that greater attention should be paid to the emergency services by geographers.     

1999—2008年巴彦诺尔公气象要素对水面蒸发影响的灰色关联分析

利用巴彦诺尔公基准气候站1999—2008年非冻结期(4—10月)E-601B大型蒸发器观测的蒸发量,以及同步观测的常规气象要素,采用灰色关联方法初步探讨温度、风速、日照时数、气压等气象要素对水面蒸发的影响程度。结果表明,气象要素对巴彦诺尔公地区蒸发量影响程度从大到小的排序依次为温度、日照时数、气压和风速。     

空间数据库引擎(SDE)在地图资源数据库管理中的应用

随着地图资源数据在各个领域深入广泛的应用,作为应用基础和前提的地图资源数据的管理显得尤其重要。本文介绍了利用ESRI公司的空间数据库引擎(SDE),实现地图资源数据库管理的方法。该方法改变了传统的文件管理方式,不仅提高了地图数据资源的安全性,而且能使地图数据便于管理和使用,同时实现了局域网内地图资源的共享。     

面向对象技术在城市配电网GIS数据库中的应用

针对当前城市配电网基础数据管理的现状,本文阐述了利用面向对象与关系数据库技术设计城市配电网基础数据库的重要性,重点讨论了城市配电网GIS数据库的基本模型,包括数据库基本构架、编码方案、属性数据与空间数据、系统对象模型向数据库基本模型的映射,为构建城市配电网GIS应用平台及实现企业级的数据共享提供了基础。     

Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics

Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs. These gazetteers differ in their overall coverage, underlying data sources, provided functionality, and geographic feature type ontologies. Consequently, place types that share a common name may differ substantially between gazetteers, whereas types labeled differently may, in fact, specify the same or similar places. This makes data integration and federated queries challenging, if not impossible. To further complicate the situation, most popular and widely adopted geo‐ontologies are lightweight and thus under‐specific to a degree where their alignment and matching become nothing more than educated guesses. The most promising approach to addressing this problem, and thereby enabling the meaningful integration of gazetteer data across feature types, seems to be a combination of top‐down knowledge representation with bottom‐up data‐driven techniques such as feature engineering and machine learning. In this work, we propose to derive indicative spatial signatures for geographic feature types by using spatial statistics. We discuss how to create such signatures by feature engineering and demonstrate how the signatures can be applied to better understand the differences and commonalities of three major gazetteers, namely DBpedia Places, GeoNames, and TGN.