Next-Generation Data-Driven Analytics- Leveraging Diagnostic Analytics In Model Based Production Workflows
Authors: Reddicharla Nagaraju , Ali Mayada Ali , Cornwall Rachelle , Shah Ankit , Soni Sandeep , Isambertt Jose , Sabat Siddharth 
Published: March 2019

Abstract
Digital oil fields have seen major advancements over the past ten years, with the goal of integrating and optimizing the loops of production operation, production optimization, well and reservoir surveillance based on real-time data and model-based workflow automation capabilities. This paper discusses how ADNOC onshore has successfully implemented model-based digital oil field workflows in all its producing fields and describes the process of migrating these workflows to a data-driven platform for improved decision making.
In the existing workflows, data-driven diagnostic analytics are applied to validate well performance and accelerate the process of identifying underperforming wells and inefficiencies. These data-driven diagnostic analytics were implemented on a digital oilfield workflow platform where data is aggregated from disparate data sources consisting of non-real time well data, well events, well test history, MPFM, interpreted PTA, reservoir simulation, well integrity and wells tie-in data, along with continuous real-time sensor and model-generated data. The analytics are mapped with workflows and asset hierarchy. The linear regression method is used to forecast trends for water cut and GOR based on historical data.
Diagnostic analytics have been successfully configured for a giant onshore field having more than a thousand wells and multiple reservoirs. The alarm diagnostic map is generated based on tolerance and difference with exceptions. The solution framework has a common data abstraction layer and integration. A built-in visualization engine allows customization based on user preferences, linking multiple screens and analytics. Well test validations are improved for non-instrumented wells by using diagnostic based on more than 10 years of well test history. Well level allocation analytics allow comparisons between real-time export meter and terminal figures at the same timestamp, based on well models. For model calibration, wellhead pressure estimation from the last valid model was introduced. Well surveillance and management diagnostic analyze wells which are operating on critical/sub-critical condition and increasing water cut based on models and measured data. The combination of reservoir simulation data, PTA, bottom-hole surveys and estimated data from well models provides insights to validate quality of simulation data and reduce uncertainty in well models. Compartment and reservoir-wise VRR diagnostic enable asset operators to take faster remedial actions for reservoir performance management. These analytics complemented traditional model-based automated workflows for identifying wells for optimization.
A digital oilfield solution platform has been leveraged to implement diagnostic analytics in the first phase and to provide a road map to migrate it to next-generation data-driven platform that has more predictive capabilities. This paper discusses solutions and data integration frameworks, analytics visualization, integration with model-based workflows, value cases and the road map ahead.