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High frequency torsional oscillation (HFTO) is still one of the most disruptive drilling dysfunctions we encounter. Vibrations are observed with fundamental frequencies as high as 400 Hz and torque sweeps from 0 to 7000 lbf.ft. The resulting damage includes drilling collar cracking, damaged electronics, and backed-off tools. By measuring the amplitude and the fundamental frequency of this dysfunction, we present a model to characterize its drivers. This is a critical step in defining the mitigation strategies. Although there are a multitude of drilling dynamics tools deployed to record these effects, the nature of HFTO, with large amplitude harmonics on top of the fundamental modes, means that simply deploying a sensor and data acquisition tool is not sufficient to characterize the dysfunction. There are critical requirements for these recorders in terms of sampling frequency and anti-aliasing filters, without which a unique interpretation of the dynamics is impossible. We have a next-generation MWD tool that will detect HFTO. By calculating a fast fourier transform (FFT) in real time, it will also deliver a log of HFTO throughout the operation, that can be delivered to the driller in real time. With this we have developed and demonstrated a suite of mitigation strategies. These are specific to the type of HFTO detected and include increasing the collar speed or reducing the WOB (for Type 2) or reducing the rate at which the WOB is increased (for Type 1). We also show that by changing the contact points on the tool to reduce the side force (friction), the operator can mitigate the Type 2 HFTO and achieve a considerable improvement on this drilling dysfunction and its impact.
High frequency torsional oscillation (HFTO) is still one of the most disruptive drilling dysfunctions we encounter. Vibrations are observed with fundamental frequencies as high as 400 Hz and torque sweeps from 0 to 7000 lbf.ft. The resulting damage includes drilling collar cracking, damaged electronics, and backed-off tools. By measuring the amplitude and the fundamental frequency of this dysfunction, we present a model to characterize its drivers. This is a critical step in defining the mitigation strategies. Although there are a multitude of drilling dynamics tools deployed to record these effects, the nature of HFTO, with large amplitude harmonics on top of the fundamental modes, means that simply deploying a sensor and data acquisition tool is not sufficient to characterize the dysfunction. There are critical requirements for these recorders in terms of sampling frequency and anti-aliasing filters, without which a unique interpretation of the dynamics is impossible. We have a next-generation MWD tool that will detect HFTO. By calculating a fast fourier transform (FFT) in real time, it will also deliver a log of HFTO throughout the operation, that can be delivered to the driller in real time. With this we have developed and demonstrated a suite of mitigation strategies. These are specific to the type of HFTO detected and include increasing the collar speed or reducing the WOB (for Type 2) or reducing the rate at which the WOB is increased (for Type 1). We also show that by changing the contact points on the tool to reduce the side force (friction), the operator can mitigate the Type 2 HFTO and achieve a considerable improvement on this drilling dysfunction and its impact.
High frequency torsional oscillation (HFTO) is a major contributing factor to the drilling dynamics-related bottomhole assembly (BHA) failures. These failures are costly because they not only damage the drilling tools but also cause significant nonproductive time (NPT) to the operational activity. It is therefore highly desirable to measure and mitigate this dysfunction in real time. We have built a high-performance drilling dynamics module, which enables real-time or recorded mode measurements of this HFTO phenomenon. Using this measurement, we can deliver actionable insights to the rig or the rig crew in real time. With data from more than 3000 runs, we can build a clearer understanding of the real characteristics of HFTO, its drivers, and the effects it has on our drilling systems. We show that with increasing Weight on Bit (WOB) the amplitude of the HFTO will increase. However, at high WOB the dysfunction amplitude will drop, while the ROP continues to rise. As such there is a sweet spot with high ROP and low HFTO. By integrating this database of drilling dysfunctions with our records of tool damage and nonproductive time, we can map the effect of HFTO onto different failure criteria. As a result, we can define new and better operational standards and generate real-time insights into what damage is more likely to occur and how to change drilling parameters, if needed, to prevent it or determine if the vibration will pass without incident. We also show that although the rotary steerable tools we use are sensitive to the effect of HFTO, our measurement while drilling (MWD) tools are not. For this latter group the probability of damage is the same for runs with and without HFTO. This paper discusses the method and results for this study.
In the continuous pursuit of enhancing the efficiency and safety of drilling operations, the optimized placement of downhole sensors has emerged as a pivotal area of innovation. This paper presents an optimized configuration for sensor placement, meticulously designed to advance the understanding and control of drilling dynamics. By integrating high-resolution sensors capable of measuring three-axis acceleration, revolutions per minute, weight on bit (WOB), and torque, an optimized approach to monitor and analyze a drilling system's performance can be established. The methodology emphasizes steering efficiency, system integrity, and scenario-specific relevance, catering to a wide range of operational challenges. Several key scenarios are addressed, including high-frequency torsional oscillations (HFTO) detection and mitigation, backward whirl detection indicative of motor failure, motor micro stall conditions, reamer deployment strategies, comprehensive performance optimization, and stick/slip detection for preventing twist-offs. Based on this, the sensors are positioned at the most sensitive spots within the bottom hole assembly (BHA), ensuring a precise characterization of the excitation and distribution of critical stresses. Combined analysis of field data and 4D (3D in time domain) drilling dynamic simulations (Chen et al, [2015]), has affirmed the efficacy of the recommended sensor placement strategy in improving drilling efficiency and reducing operational hazards. The simulations particularly highlighted the capability to infer measurements from sensor data across different BHA components. It was also determined that WOB and torque on bit measurements from elsewhere in the BHA could be confidently inferred at the drill bit through extrapolation. Field data analysis further revealed that capturing burst acceleration data at the drill bit, integrated with a digital solution, effectively identifies HFTOs, facilitating their mitigation. Additionally, positioning a torque sensor just above the steering unit, where significant bending occurs, can prove instrumental in predicting HFTO-related fatigue life with considerable precision. Leveraging field data alongside advanced 3D dynamic simulations has led to the identification of an optimized sensor positioning. This versatility across numerous drilling scenarios, ensures the integrity of the drill bit while seamlessly integrating with the steering mechanism.
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