Big Data Guided Unconventional Digital Reservoir Energy Ecosystem and its Knowledge Management

IF 2.4 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE Pacific Asia Journal of the Association for Information Systems Pub Date : 2021-04-12 DOI:10.17705/1PAIS.13101
S. Nimmagadda, Neel Mani, Torsten Reiners, Lincoln C. Wood
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引用次数: 2

Abstract

Abstract Background: In shale basins, petroleum systems are complex; they hold data sources in Big Data scales. The motivation of research lies with the facts of exploring effective inherent connectivity between unconventional petroleum systems. The connectivity between energy reservoir systems is ambiguous within a distinctive petroleum ecosystem. Heterogeneity and multidimensionality of unstructured data sources are additional challenges, precluding systematic modelling of diverse petroleum systems and their data integration process, including growing demand for storage systems. The research aims to establish the knowledge-based connectivity between petroleum systems through Information System (IS) articulations, visual analytics and data management. Method: We investigate the knowledge-based IS guided exploration and production systems to explore the connectivity between diverse unconventional petroleum systems and forecast the reservoir energy. We articulate Design Science Information System (DSIS), bring various IS artefacts, unify multiple domains of petroleum provinces and analyze the associativity between petroleum systems. In addition, use, reuse, effectiveness and interoperability are utility properties of IS artefacts that we evaluate. We implement IS solutions in the oil and gas industries to facilitate database management and reservoir energy exploration. Results: We simulate DSIS as an Unconventional Digital Petroleum Ecosystem (UDPE) as it allows us to investigate and ascertain the interplay between petroleum systems’ elements and processes. Metadata cubes are computed for data views to visualize, interpret, and implement IS articulations in energy systems. We compute the structure and reservoir attribute views for interpreting energy-driven petroleum systems, prospect evaluation and business-knowledge management with a viable DSIS solution. Conclusions: The DSIS emerges as a knowledge-based digital ecosystem innovation, demonstrating how it can effectively interconnect geographically controlled petroleum systems. Its development, in the exploration of unconventional shale basins, is a knowledge-based reservoir-energy management solution. This research is beneficial to IS practitioners who wish to pursue energy research in reservoir ecosystem contexts.
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大数据引导非常规油藏数字化能源生态系统及其知识管理
背景:页岩盆地含油气系统复杂;它们拥有大数据规模的数据源。研究的动机在于探索非常规油气系统之间有效的内在连通性。在一个独特的石油生态系统中,能量储层系统之间的连通性是模糊的。非结构化数据源的异质性和多维性是另一个挑战,这妨碍了对各种石油系统及其数据集成过程进行系统建模,包括对存储系统的需求不断增长。该研究旨在通过信息系统(IS)连接、可视化分析和数据管理,在石油系统之间建立基于知识的连接。方法:研究基于知识的IS引导勘探生产系统,探索不同非常规油气系统之间的连通性,预测储层能量。阐述了设计科学信息系统(DSIS),引入了各种信息系统构件,统一了石油省的多个领域,分析了石油系统之间的关联性。此外,使用、重用、有效性和互操作性是我们评估的IS工件的实用属性。我们在石油和天然气行业实施IS解决方案,以促进数据库管理和油藏能源勘探。结果:我们将DSIS模拟为非常规数字石油生态系统(UDPE),因为它允许我们调查和确定石油系统元素和过程之间的相互作用。元数据立方体是为数据视图计算的,用于可视化、解释和实现能源系统中的IS连接。利用可行的DSIS解决方案,计算结构和储层属性视图,用于解释能量驱动的石油系统、前景评价和业务知识管理。结论:DSIS是一种基于知识的数字生态系统创新,展示了它如何有效地互连地理控制的石油系统。它的发展,在非常规页岩盆地的勘探中,是一种基于知识的储层能源管理解决方案。这项研究对希望在水库生态系统背景下进行能源研究的信息系统从业者有益。
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4.10
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33.30%
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