Shale-gas ontology, a robust data modeling methodology for integrating and connecting fractured reservoir petroleum ecosystems that affect production complexities

Abstract

Authors propose robust data warehousing and mining approach supported by ontology that can integrate data attributes of associative-fractures of multiple (dimensions) horizons from different types (of different geological and production regimes) of wells and fields, periodically (longitudinal dimension) and geographically (distantly, lateral dimension) located within a producing basin and or extended to multiple basins. Authors attempt to make connectivity among structure, reservoir and production data dimensions and their attributes through their common data instances. In other words, integration is done by mapping and modelling of conceptually (more logically) interpreted relationships among multidimensional inter-dependent data instances of structures (including reservoirs) and attributes through their similar data property instances (and or dissimilar) that are described from different fracture systems. As an example, data mining procedures, if can forecast and separate out the rock stress data patterns of shale-prone environments, so that driller or well planner can identify or plan in advance the types of fracture systems that are being drilled. The proposed methodology is robust and can resolve issues relevant to deviation and smart drilling in the fractured reservoir systems. Ontology based multidimensional data warehousing and mining can integrate and make connectivity among varying common and conceptualized relationships, associated with structure, reservoir and production data that describe their complexity in shale-gas environments. If the proposed methodology is successful, it can be applied in any basin not only for conventional reservoir ecosystems, but fractured including tight-gas and gas-hydrate systems worldwide.