Abstract
The ROSA Project is focused on the investigations of oil and gas industry progress in Russia and other countries. The primary objective is to examine and evaluate data on worldwide hydrocarbon occurrences. Major aim is to construct a comprehensive map of the allocation of oil and gas fields with large reserves for further analogy estimation and reconstruction of geological history. The main contribution of this work is the development of a multidimensional and multilevel database and the corresponding GIS Project for visualization. The set of multidisciplinary backgrounds in combination with a spatial algorithmic tools are used as a basis for an analytical study of worldwide hydrocarbon occurrences and estimation establishment of petroleum industry.
Highlights
Creating and support of verified databases is one of the priorities for the development of geoinformatics
Our goal is to introduce and combine heterogeneous data in a unified manner and to represent oil and gas information that is well coordinated through each of the FAIR principles
Adherence to the principles detected in the course of our studies provides insight and guidance into where challenges lie and where opportunities exist
Summary
Creating and support of verified databases is one of the priorities for the development of geoinformatics. A database provides the availability of reliable and comparable data as a prerequisite for effective analysis on issues affecting oil and gas industry. Before approaching the issue of elaborating the idea of creation a thematically oriented database, the authors solved a number of challenges related directly to the data – their search, collection, systematization, storage and management for the purpose of further analysis. The primary objective of ROSA GIS Project is to investigate and evaluate the distribution of oil and gas in the world, identify their accumulations, compare the history of geological development and conduct a largescale analytical study. That opens new challenges in the way we search, manage and analyze data. Presented approaches could be further applied to other fields where the data is not well distributed within research community
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