Optimal Matching of Bit and Reamer for Increased Reliability of Hole-Opening BHAs

铰刀 扭矩 可靠性(半导体) 控制理论(社会学) 计算机科学 结构工程 数学 工程类 机械工程 人工智能 物理 量子力学 功率(物理) 控制(管理) 热力学
作者
Armin Kueck,Mohamed Ichaoui,Christian Herbig,Andreas Hohl,Georg‐Peter Ostermeyer,Hanno Reckmann
标识
DOI:10.2118/188709-ms
摘要

Abstract Mechanical loads in hole-opening BHAs result in tool failures and generate maintenance costs and non-productive time. This paper presents a method to increase the reliability of hole-opening BHAs by optimally matching the bit and reamer. The weight and torque distribution between the bit and reamer is predicted using a stationary load model. New quality load curves facilitate the evaluation of bit-reamer combinations in a user-friendly way. The model and the load curves are validated on a unique set of field data, enabling determination of the model's accuracy. The model is based on the mechanical specific energies at the bit and at the reamer. The model assumes the RPM and rate of penetration to be constant, the BHA is rigid in the axial and torsional directions and the lateral movement is blocked. Quality load curves are deduced that depict the load distribution in one plot. The model is validated on a unique data set that includes several high-precision measuring tools placed along the drill string. The unconfined compressive strength over depth that usually is not measured in other runs is available. The data set enables precise determination of the axial forces and torques directly at the bit and at the reamer. The observed mechanical specific energy, drilling efficiency, and aggressiveness of both cutting tools over depth are measured. The model and the quality curves are used to predict the weight and torque distribution depending on the formation type at the bit and at the reamer. A comparison of the prediction to the measured data shows that the weight distribution is predicted with an error of 2% and the torque distribution is predicted with an error of 10.8%. The model accuracy is determined by introducing uncertain parameters into the model. The load predictions are again compared to the measured data. Using the coarse parameter set, the mean prediction error increases to 13%, which is very good, considering the simplifying assumptions of the model. The validated model and the new quality curves enable an optimal choice of bit and reamers. The presented approach is fast and user-friendly and perfect for an application in advisory software in the well-planning phase. The increased reliability due to less mechanical overloads leads to reduced maintenance costs and less non-productive time of the reamer BHAs.
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