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Advanced Prognostic Technique for Improving the Drilling Performance of Downhole Tools

机译：提高井下工具钻井性能的高级预后技术

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Modern drilling equipment faces increasingly severe environments, with bottom hole temperatures excessing 200°C and high vibration stresses. Meanwhile, customers continuously demand high reliability to prevent bottom hole failures, which result in one half or two days of nonproductive time (NPT) on drilling rigs costing $100K-$1M per day [1]. However, the current periodic maintenance strategy is insufficient and costly to meet the new challenges. From a cost-effective perspective, developing a conditional-based maintenance (CBM) prognostic model to evaluate the health condition of bottom hole assembly (BHA) tools becomes important and necessary for the oil and gas industry. This paper presents a CBM prognostic model for assessing the life consumption (LC) of BHA tools. Based on the analysis of physics of failure (PoF) [2], there are four major impacts on electronics reliability, i.e., lateral vibration (LV), stick slip (STI), temperature (TEMP), and axial vibration (AX) [1][2]. The CBM prognostic model described in this paper is acumulative damage model with weibull distribution, which is a linear regression model synthesizing the four impacts.To estimate the parameters of the model, the maximum likelihood estimation (MLE) is utilized. The confidence interval and the Fisher information matrixtheories are applied to assess the LC intervals of the tools. The developed model will allow service providers to make more efficient decisions to reduce operation cost and increase downholoe tool availability and reliability.
机译：现代钻井设备面临着越来越严峻的环境，底部孔温度超过200°C和高振动应力。同时，客户不断要求高可靠性以防止底部孔故障，这导致钻井平台上的一半或两天的钻井平台（NPT）每天花费100k-$ 1m [1]。但是，目前的定期维护策略不足，昂贵，以满足新的挑战。从成本效益的角度来看，开发基于条件的维护（CBM）预测模型来评估底部钻具组合（BHA）工具的健康状况成为石油和天然气行业的重要和必要的。本文介绍了评估BHA工具寿命（LC）的CBM预后模型。基于（POF）失败的物理的分析[2]，则对电子的可靠性，即，横向振动（LV），粘滑（STI），温度（TEMP），和轴向振动（AX）[四大影响1] [2]。本文中描述的CBM预后模型是与Weibull分布的辐射损伤模型，这是一个线性回归模型，其合成了四个影响。要估计模型的参数，使用最大似然估计（MLE）。置信区间和Fisher信息矩阵用于评估工具的LC间隔。开发的模型将允许服务提供商做出更有效的决策，以降低运营成本并提高较高楼层工具可用性和可靠性。

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