In the recurring need to optimize drilling operations and reduce costs, a full RTOC (Real Time Operations Center) solution was deployed as part of the organization structure. To bring accurate, automatic and relatable data capture from surface sensors, the RTOC introduced a digital twin approach to improve field to town collaboration. The paper will demonstrate the benefits brought to operations by the solution in terms of risk identification and lessons learnt.
RTOC digital twin solution integrates standard physical models’ workflows for hydraulics, torque and drag with advanced solutions using machine-learning algorithms. Capitalizing on operations recognition algorithm, the solution identifies thresholds and calibrates parameters to automatically classify operations into "Rig States" and "Drill States". The algorithm is trained to identify operational sequences and can derive complex measurements like downhole weight-on-bit and torque that are in turn fed into different workflows. This holistic event-based torque and drag baseline determination is used to define hole cleaning roadmap with minimum manual inputs.
RTOC receives, processes and publishes the real time data on through its platform for all drilling and completion operations. This continuous process has enabled drilling operations team to assess and intervene on a need basis thanks to the clear event identification it offers. Amongst the digital workflows, the hole cleaning roadmap, combines modelled and automatically identified torque and drag data points rendered and shared with the stakeholders to ensure the capture of deviations and framing of potential risks to acceptable levels through a common decision platform. The clear output of single identifiable drilling event (such as pick up, slack off and free rotating weight) provides constant fact-based data for an adequate protocol to run casings and liners and refine engineering designs. In turn it has enabled to break casing and liner run records in their different operating fields. The drilling efficiency roadmap rely on quantitative algorithm and reliable output of downhole weight-on-bit, downhole torque and mechanical specific energy with automatic calibration, without user intervention nor bottom-hole-assembly modelling, allowing to substitute actual downhole measurements. This has been a performance enhancer in the improvement of rate of penetration regardless of the availability of downhole sensors.
This new approach based on modern data science and digital twin based on a robust method, provides with a consistent and clear outcome regardless of service providers involved in the direct operations. It was trained, tested and validated prior to deployment, on more than 80 wells. This has also made possible the introduction of other algorithmic developments for Realtime dynamic modelling.