Real-Time Underreamer Vibration Predicting, Monitoring, and Decision-Making Using Hybrid Modeling and a Process Digital Twin

Abstract

Summary In hole enlargement while drilling (HEWD) operations, underreamers are used extensively to enlarge the pilot hole. Reamer wipeout failure can cause additional bottomhole assembly (BHA) trips, which can cost operators millions of dollars. Excessive reamer shock and vibration are leading causes of reamer wipeout; therefore, careful monitoring of reamer vibration is important in mitigating such a risk. Currently, downhole vibration sensors and drilling dynamics simulations (DDSs) are used to comprehend and reduce downhole vibration, but vibration sensors cannot be placed exactly at the reamer to monitor the vibrations in real time. DDSs are difficult to calibrate and are computationally expensive for use in real time; therefore, the real-time reamer vibration status is typically unknown during drilling operations. A process digital twin using a hybrid modeling approach is proposed and tested to address the vibration issue. Large amounts of field data are used in advanced DDSs to calibrate the HEWD runs. For each HEWD section, calibrated DDSs are performed to comprehend the downhole vibration at the reamer and downhole vibration sensors. A surrogate regression model between reamer vibration and sensor vibration is built using machine learning. This surrogate model is implemented in a drilling monitoring software platform as a process digital twin. During drilling, the surrogate model uses downhole measurement while drilling (MWD) data as inputs to predict reamer vibration. Wipeout risk levels are calculated and sent to the operators for real-time decision-making to reduce the possibility of reamer wipeout. Large volumes of reamer field data, including field recorded vibration and reamer dull conditions were used to validate the digital twin workflow. Then, the process digital twin was implemented and tested in two reamer runs in the Gulf of Mexico. A downhole high-frequency sensor was placed 8 ft above the reamer cutting structure in one field run, and the recorded sensor vibration data and corresponding reamer dull conditions showed a very good match with the real-time digital twin predictions in a low-vibration scenario. Cases in high vibration are needed to fully validate the feasibility and accuracy of the digital twin. State-of-the-art downhole sensors, DDS packages, large amounts of field data, and a hybrid approach are the solutions to building, calibrating, and field testing the reamer digital twin to ensure its effectiveness and accuracy. Such a hybrid modeling approach can not only be applied to reamers but also to other critical BHA components.

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section

“…A hybrid modeling approach was used to build, calibrate, and field test a digital twin of a reamer, including field-recorded vibration and dull reamer dull conditions. The digital twin showed a good match with the real-time digital twin predictions in a low-vibration scenario . A multidimensional optimization engine was developed through machine learning for optimizing real-time drilling operations by reducing nonproductive time events such as vibration, stick–slip, and directional divergence.…”

“…The digital twin showed a good match with the real-time digital twin predictions in a low-vibration scenario. 32 A multidimensional optimization engine was developed through machine learning for optimizing real-time drilling operations by reducing nonproductive time events such as vibration, stick–slip, and directional divergence. The tool is developed to monitor vibration in downhole drilling systems [measurement while drilling (MWD) or rotary steerable (RSS)].…”

Digital Twin-Enabled Internet of Vehicles Applications

“…With the increasing interest of researchers in machine learning, machine learning has been able to handle more complex tasks. • Hybrid model [115]: It creates a multi-dimensional and comprehensive digital model of physical entities by mixing different models. Physical models can be used to create physical properties of entities, and machine learning models can be used to predict and simulate the behavior of the system.…”