“Sustainable Spatial Architecture for Geo Engineering Data and Workflows”

Spatial attributes are pervasive in geologic and engineering data. Competitive solutions require a sustainable architecture for this data with documented workflows in order to increase confidence and alleviate risk. The four components of a sustainable spatial architecture are; data accessibility, a standard data model, portal consumption and Geographic Information System (GIS) access, along with data maintenance and business rules. A full solution for management of spatial attributes includes collection from field and public domain sources, quality assessment and control, storage in standardized data models, distribution to analysis applications, and capture in knowledge management and audit systems. Each component and stage can impact financial performance. Site assessments and a standard methodology for documentation of processes and components were used to compare solutions and value statements for multiple domestic and international operators. Accessibility of data addresses quality, accuracy, and confidence related to spatial data. The economic impact is lack of end-user confidence in data tools, and the cost of re-acquiring data. The data model provides feature class intelligence, naming conventions, and attribute accessibility, a standard taxonomy, and a method to move petroleum engineering data into a world of points, lines and polygons. Utility is measured in lost opportunity costs of not using available data in analysis, and inconsistent data causing poor drilling decisions. Portal access through service oriented architectures delivers visual and automated quality control of multiple data sources and is documented to save engineering time spent on data discovery and manipulation. Business rules serve to formalize data ownership and governance and support an intelligent synchronization process that maintains validated corporate spatial data. This single source of truth leads to reduced risk for engineering decisions. Successful solutions require all described components plus repeatable and auditable workflows for extracting well, reservoir and seismic spatial attributes from robust industry standard models. This study allows petroleum engineers to determine the maturity of their current and legacy solutions, realizing a goal of mitigating risk, improving workflows, and lowering costs within a scalable, sustainable Spatial Data Architecture.

THOMAS W. RIPLEY
Managing Consultant, SAIC
Over twenty five years as a GIS Architect, GIS/SDE Database Project Leader, GIS project Manager, and Geological Subsurface Modeling Software Account Executive supporting the Oil and Gas industry.
Member of the Petroleum Steering committee for the ESRI Petroleum User Group and have presented technical papers at various conferences, including SPE 2008 and ESRI 2001.