Conventionally offset well studies are performed by individuals where the results depend very much on visual perception, interpretation, and experience. In the specific cases for predicting the dogleg severity (DLS) output, the offset well study will take time proportionate to the volume of input, with the results being averaged out and contain high tolerances. In specific projects, these tolerances are larger than accepted, encouraging the service provider to utilize conservative solutions such as rotary steerable system (RSS) with high DLS capability in order to reduce the residual risks. These solutions can often be more costly in terms of maintenance and may add unnecessary tortuosity to the hole leading to issues during execution. This paper explores the concept of using machine learning (ML) to perform offset well study and defining key parameters affecting the DLS output. This concept consolidates the vast volumes of data that have been acquired while drilling and defines the relationship of each parameter to the final output of DLS. The first analysis reviewed five offset wells and found a multivariable correlation between applied drilling parameters to the DLS output. This correlation was then applied in 6 boreholes (3 multilateral wells), observing consistent DLS output increase by 50% using the same technology and optimal drilling parameters. The second analysis uses the same process to determine a planning DLS limit in a curve section over different formations. This paper demonstrates the potential of ML in offset well studies and beyond to predict behavior and define the relationship in a big data environment.
High-frequency torsional oscillation (HFTO), a highly destructive drilling vibration mode, causes severe fatigue damage to drilling tools. Recent industry discoveries suggest techniques for reducing HFTO through adjustments of drilling parameters; however, these techniques normally result in detrimental effect on rate of penetration (ROP) or equipment operated out of specifications. Deeper understanding of this dysfunction resulted in the development of a special HFTO dampening tool to lessen the dysfunction without limiting the drilling parameters needed to maximize performance. Drilling dynamics measurements were obtained from high-frequency logging tools placed at strategic positions in motorized and standalone rotary steering system (RSS) bottomhole assemblies (BHA). The data obtained from these measurements were analyzed to better understand HFTO characteristics in multiple geological environments. A transient drilling dynamics model was then built to reproduce HFTO motion and help understand the loading conditions under this vibration mode. This information was used to define the best technology and components’ characteristics for the design of the dampening tool to effectively lessen HFTO over a wide frequency range. The transient model was later upgraded to include the physics principle and technical specifications of the dampening tool so that the operational results could be scientifically validated. High-frequency signals capturing variations of revolutions per minute (RPM), axial and tangential acceleration, torque, axial loading, and internal pressure have been used to characterize HFTO, either directly or indirectly. Crossplots of the signals from the HFTO cycles showed intriguing patterns of phase shifts between the signals. Torsional strain and displacements are distributed along the BHA based on HFTO mode shapes, which can be predicted by the transient drilling dynamics model. Excessive torsional strain and kinetic energy caused by the HFTO are mostly restrained at the lower BHA, especially in motorized RSS applications with the motor acting as a reflector. Vibration isolation is a valid strategy to minimize its destructive impact on BHA tools. Nevertheless, not all components can be placed above an isolator due to BHA design constraints. A dampening tool has been designed and optimized to operate as standalone or combined with isolators to alleviate HFTO over a wide frequency range. Guided by modeling, one or multiple tools can be placed immediately below a mud motor for a motorized BHA or at selected locations with maximum RPM variation for rotary BHAs. After multiple field tests, the tool consistently delivers lower HFTO magnitudes than offsets. Significant performance gains were achieved as the operating parameter ranges were extended. In this paper, additional insights into HFTO characteristics and their effect on drilling systems are presented. A modeling procedure has been developed to predict the most likely HFTO modes and help design BHAs and drilling tools. A robust dampening tool has been developed and field tested to effectively diminish HFTO and improve drilling performance.
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