This paper presents a novel approach for drilling analysis, optimization, and bottomhole assembly (BHA) component maintenance. The approach uses automation to report shift statistics and process parameter evaluations based on real-time drilling data. Included in the approach are data quality assessment, risk assessments for shock and vibration, and prognostics and health management (PHM) modules for BHA components.

The proposed core algorithm evaluates the content of the various time-based drilling channels and then outputs relevant statistics by classifying the drilling events. This hybrid approach combines the data-driven approaches with machine-learning techniques. Shock and vibration risk assessment algorithms derive the risk levels for each drilling operation under consideration in accordance with industry standards. The integrated PHM algorithms analyze the cumulative risk of component fatigue failure and produce BHA component health information using unique, industry-specific, physics-based, appropriate models.

The set of advanced algorithms are successfully integrated into the system level before they are released to commercial users. This solution provides users with a comprehensive set of high-level information necessary to analyze drilling jobs. The drilling journal is a key output of the solution, playing a crucial role in offset analysis, trajectory design, BHA design, etc. Enabling proactive thinking and decision making helps the user anticipate future actions needed for subsequent job activities.

In addition to providing the drilling crew with valuable insights about the cumulative exposure conditions of equipment, the solution also formalizes the drilling process through business systems for equipment exposure analysis. The insights and formalization help achieve full field adherence and promote standardization and consistency within decision-making processes.

The solution also includes PHM modules that standardize decision-making processes concerning reusing BHA components, helping to reduce field fatigue-related failures. Remarkable results were achieved with the commercial exploitation of the PHM algorithm for the mud motor power section. Implementation resulted in a dramatic decrease in motor-related field failures.

Overall, this innovative and complex solution provides an efficient and reliable method to analyze drilling job data, promote field adherence, and reduce field failures. This first-time published approach distinguishes itself by means of a sophisticated use of real-time drilling data to extract decisive statistics for ongoing monitoring and rapid decision making during the drilling operation. With the aid of advanced PHM models, the proposed innovative solution significantly enhances the cumulative commercial impact.

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