Drilling Optimization: Utilizing Lifetime Prediction to Improve Drilling Performance and Reduce Downtime

Abstract

The capability to optimize drilling performance by predicting the life of drilling components is integral to preventing costly downhole tool failures and ensuring success of any drilling operation. Drilling tools are subject to various parameters such as vibration, temperature, revolutions per minute (RPM) and torque. These parameters can greatly fatigue even the most robust tool depending on the where and how the tool is operated. As a result, there is a need to predict time to failure of components operating in a downhole drilling environment. Analyzing operational data, inclusive of the parameters above, prior to or during maintenance actions and before starting drilling jobs, provides unique insight into how to improve the drilling performance of tools and to reduce downtime. Life prediction provides a cutting-edge way to identify precursors to costly failures in the field and enables proactive guidance during maintenance periods for parts which may otherwise have been disregarded strictly on maintenance procedures. Statistical models that relate operating environment to the component life and are derived from failure data of fielded components, introduce a new way to optimize the efficiency of drilling tools. Utilizing lifetime prediction to optimize drilling performance is a groundbreaking methodology developed to determine life of components operating in benign and harsh drilling environments by incorporating statistical aspects such as those caused because of variation in operating stress and maintenance upgrades. Since the algorithm utilizes field data, the need for costly laboratory experiments are also eliminated. Each model developed is unique to the specified part and can be calibrated for the best fit. In this methodology, a Bayesian-based model selection technique is developed that incorporates operating environment variables after each successful drilling run to dynamically select a model that gives the best survival probability for that component. Dynamic model selection ensures maximum utilization of a component, while avoiding failure to improve the overall reliability of the tool while in the field. The paper describes the methodology used to estimate the life of components in drilling systems by employing operational data, drilling dynamics and historical information.

The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

“…With drilling technology advancement, oil and gas drilling activities more frequently occur in the rock layer of thousands of meters depth and severe downhole conditions [1]. The challenging downhole environment includes temperature exceeding 200 C, shock and vibration levels surpassing 15 g, pressure beyond 207 MPa, strong abrasive formation, horizontal path instead of conventional vertical bore hole, and others [2,3]. Figure 1 illustrates several typical features of drilling activities.…”

“…The harsh downhole conditions are around or beyond operating tools' design specification constraints, and severely damage even the sturdiest and most reliable components, such as printed circuit board Fig. 1 Illustration of drilling activity [3] 1 assemblies parts in logging-while-drilling (LWD) and measurement-while-drilling (MWD) tools [2,4,5]. This phenomenon leads to the increased tool failure rate, system downtime, nonproductive time, higher maintenance and repair costs, and lowered operation reliability for both drilling operators and service providers [3,4,6].…”

“…This phenomenon leads to the increased tool failure rate, system downtime, nonproductive time, higher maintenance and repair costs, and lowered operation reliability for both drilling operators and service providers [3,4,6]. Consequently, the analysis, evaluation, and prediction of drilling tool failure and operation reliability in the complicated downhole conditions, as well as the methodology for optimizing maintenance plan and reducing lifecycle cost, has become an emerging research topic in reliability engineering field [2,4,7].…”

“…Due to the system and human factor limitation, the interactive impact of multiple parameters to different drilling tools cannot be accurately measured [3,4]. However, the previous research reveals that downhole vibration is one important factor to weaken tool robustness, reduce tool reliability and life expectancy [2,4,5,7,9]. In addition, downhole vibration can also cause excessive stabilizer wear, well trajectory deterioration, high-frequency noise, lower rate of penetration (ROP), decreased measurement accuracy, and other serious issues [10].…”

“…These topics include tool reliability assessment, modeling, prediction, failure analysis, reliability in tool design, development, testing, maintenance planning, and lifecycle cost analysis [18]. As a consequence, more and more research efforts have been put on reliability big data analytics methodologies of drilling tools [2,6,17]. To the best knowledge of the authors, as a critical factor to affect operation reliability, the detailed role of vibration factor in drilling tool reliability big data analytics has not been systematically explored by any academic article.…”