Drilling Performance and Data Quality Control with Live Digital Twin

Abstract

The paper presents a solution to the problem of qualitative determination of actual downhole loads and drilling parameters optimization performed employing a dynamic digital well model. The problem of the surface and downhole sensors data quality is disclosed, a solution for an aggregated data QAQC and achieved results are presented. The implementation of the digital platform and the functionality of the dynamic digital twin allowed us to improve the compliance with desired regimes, enabled ensuring the safety of technological operations, allowed us to speed up decision-making while drilling and well completion and commissioning into production. The digital ecosystem allows to timely respond and control operational parameters, to improve and accurately control ROP while minimizing drilling hazards risks and premature drill bit bits wear. The incorporated dynamic digital twin in real-time allows assuring data quality, analyzing activities efficiency, and defining the optimal drilling parameters. The selection of optimal drilling parameters and an increase in ROP are carried out in real-time, based on the analysis of specific mechanical energy. Quality control of sensors plays a key role in the process of evaluating effective weight to bit and associated loads, and in identifying the current friction factor values exhibited downhole. Further on performed trend analysis of the friction factors and respective changes in key drilling parameters allows to track and prevent critical overloads of the drill string, permits to determine the risks of downhole hazards, enables evaluation of well circulation and conditioning activities efficiency in a given interval – allows reducing invisible NPT and the risks of downhole complications. The introduction of a digital ecosystem and a dynamic digital twin allowed us to bring the well construction management process to the next level. Operational response and the decision-making process has been drastically accelerated and improved. Uncertainties associated with an expert's interpretation of drilling states, and subjectivity in the opinions on the effectiveness of processes were eliminated. The negative effect of the human factor and the resulting invisible nonproductive time was minimized. In a short period, the drilling contractor was able to integrate a single digital platform, improve key performance indicators, and involve the field personnel in the full cycle of the technological process of well construction. Field and office personnel, including the driller, can work in a single digital platform, and regardless of the current operation, do always know the true downhole loads, do see the allowable operating envelope and optimal values of the hook load, surface torque, SPP, flow rate, RPM, weight, and torque on the bit, ROP and tripping speeds. The presented method of assessing the quality of the readings of measuring devices and determining the true WOB allows us to optimize the technological parameters during actual drilling. The calculation of the specific mechanical energy is performed based on effective downhole loads transferred to the drill bit. An abnormal increase in the specific mechanical energy notifies the driller to promptly correct the parameters and restore the efficient drilling process. The friction factors are automatically determined during rotation off bottom and tripping operations. Safe corridors and the operational roadmap are re-evaluated every second and are dynamically updated according to the current state of the wellbore and depths.

Data Management in Digital Twins for the Oil and Gas Industry: beyond the OSDU Data Platform

“…Existing solutions are very targeted in scope, demonstrating the added value in applications such as early detection of inflows in wells in an offshore environment [Andia and Israel 2018], production optimization [Naufal and Metra 2021], or optimization of drilling operations using historical and real-time data [Dannenhauer et al 2020]. More encompassing solutions are focused on theoretical issues rather than real implementations [Eilers et al 2020;Karpov et al 2021;Brackel et al 2018], suggesting that DT implementation in the O&G industry is still in early stages [Wanasinghe et al 2020]. Furthermore, this industry is still very focused on applications that create and manage their data for their particular purposes, with limited scope resulting in silos that make it difficult to exchange data between systems.…”