Smart Data Management for the Giant North Field
Authors: Bian Grace Lin , Abuagela Abdurraouf 
Published: December 2015

This paper describes Qatargas' new approach of linking the physical modeling and engineering workflows with a robust corporate data management backbone to improve the efficiency and effectiveness of an engineering team tasked with production planning, forecasting, allocation, optimization, well integrity, flow assurance, and reservoir management. To build a virtual integrated model of the field, component-level models such as the models for the reservoir, the wellbore, the production network and the injection network are developed and then dynamically linked to one another to ensure the consistency of the overall field-level model.

The modelling tasks require a wide range of data acquisition and processing activities (such as production and injection monitoring, well test and PLT analysis, PVT sampling and analysis, corrosion monitoring and environmental monitoring for waste water, H2S and CO2). These data are normally captured using systems in the field. Traditionally, Qatargas has operated the wells and reservoirs based on manual workflows such as manual analysis of production testing and fluid sampling, manually estimating the performance envelopes, and then calculating the optimum feed-gas volume & composition, while respecting plant's limited handling capacity of H2S% and other non-hydrocarbon components in the feed-gas. The main challenges with this approach are the very significant quantity of man-hours invested in the routine activity, and the level of accuracy in estimating the right course of feed-gas volume & composition during irregular plant operation conditions such as shutdowns, slow-downs, machine trips, as well as supporting special studies or projects.

In the new approach, the collected data are quality checked and loaded into the corporate database, as well as the various physical models. Once the physical models are established, then further analysis and "what if" scenarios can be carried out. The understanding of the field hence developed is propagated to the various discipline-specific models to ensure internal consistency between the models.

In the course of the engineering workflows listed above, the corporate data store plays an important role in orchestrating the data flows in and out of the various physical models, and preservation of the results from disparate component models. It also enables the preservation of the previous versions of the models, by keeping an audit trail of data that were delivered to each instance of multiple models of the same kind (e.g., multiple reservoir simulation models, or multiple wellbore models). The adoption of a common data management backbone between various discipline-specific modelling activities promotes the development of a shared understanding of the hydrocarbon field amongst the team members that belong to different technical disciplines. Furthermore, web enabled visualization and data display tools decoupled from the complex modelling tools enable non-specialists to develop high-level insight into the individual modelled components of the field.

The qualitative benefits of this new approach are clear to all involved; nevertheless, benchmarking data will be collected on the baseline workflows in order to quantify the overall benefits of the new approach.