A Holistic Outlook on Integrated Data Management and Architecture Philosophy in Digital Oil Field Production Workflows - Lessons Learned from 2006 - 2019 in Giant Brown Fields

Abstract

This paper illustrates integrated data management and architecture philosophy in an integrated digital oil field (DOF) production & reservoir ecosystem comprising of workflow automation implementations. Data availability, gathering, quality, validation & integration are the practical challenges where activities of extracting insightful information consumes 30% total project and sustainability efforts. The philosophy has been summarized based on challenges faced and lessons learned in four giant brown fields.

The adopted data management framework is fully aligned with DOF workflows project delivery framework of four phases. The first phase comprises of establishing approach for model-based production workflows and data strategy. The second phase involves descriptive data analytics, mainly focused on data- preparation, data-review and data validation at source (E&P, Production, Engineering & Historian/DCS databases), where information is identified, highlighted and communicated with key project stakeholders and users. Considering current state of data, a user-control driven automation mechanism is adopted such as for hydraulic model building and implemented during third phase framework development. During this phase, users perform various diagnostic analysis using initial calibration of the model to pinpoint and prioritize data quality issues that need to be resolved. This phase also includes configuration steps for standardized naming conventions, which are critical to avoid integration issues between reservoir, wells, facilities & forecasting models. The last phase is maintaining and sustaining the integrated model based DOF workflows, where some events occur on regular basis (new well-tests and surveys) taken care by workflow automation however other events occurrence is governed by planned operational activities such as new wells drilled, workover activities, facility upgrade and modifications, RMP activity schedules etc. An automated data-tracking-system was implemented to assist on timely updates. A standard asset framework and tag naming standards in real time historian automates real time data retrieval in DOF workflows.

The integrated framework defined for well measurement validation and structured approach helped not only in improving data confidence level but also in recognizing true well health condition. The presence of integrated digital oil field workflows further enhanced data validation process by bringing in physical correlation which exists among various operational parameters. The fully automated workflows have reduced manual data errors by 10% to improve efficiency. However, at the same time, semi-automatic approach was taken in which data integration with external data sources were established, but a manual option was enabled until all data gaps are identified. Several improvements have been suggested to field operators and data management teams to improve data entry processes. Real time data asset framework has reduced 70% efforts from engineers for tag configuration activities.

The Authors describe holistic perspective encompassing closed loop of data acquiring, validation, integration, architecture, real time asset framework & automation and feedback to realize and increase optimization opportunity.