No abstract
Objectives/Scope This paper presents how a brazilian Drilling Contractor and a startup built a partnership to optimize the maintenance window of subsea blowout preventers (BOPs) using condition-based maintenance (CBM). It showcases examples of insights about the operational conditions of its components, which were obtained by applying machine learning techniques in real time and historic, structured or unstructured, data. Methods, Procedures, Process From unstructured and structured historical data, which are generated daily from BOP operations, a knowledge bank was built and used to develop normal functioning models. This has been possible even without real-time data, as it has been tested with large sets of operational data collected from event log text files. Software retrieves the data from Event Loggers and creates structured database, comprising analog variables, warnings, alarms and system information. Using machine learning algorithms, the historical data is then used to develop normal behavior modeling for the target components. Thereby, it is possible to use the event logger or real time data to identify abnormal operation moments and detect failure patterns. Critical situations are immediately transmitted to the RTOC (Real-time Operations Center) and management team, while less critical alerts are recorded in the system for further investigation. Results, Observations, Conclusions During the implementation period, Drilling Contractor was able to identify a BOP failure using the detection algorithms and used 100% of the information generated by the system and reports to efficiently plan for equipment maintenance. The system has also been intensively used for incident investigation, helping to identify root causes through data analytics and retro-feeding the machine learning algorithms for future automated failure predictions. This development is expected to significantly reduce the risk of BOP retrieval during the operation for corrective maintenance, increased staff efficiency in maintenance activities, reducing the risk of downtime and improving the scope of maintenance during operational windows, and finally reduction in the cost of spare parts replacementduring maintenance without impact on operational safety. Novel/Additive Information For the near future, the plan is to integrate the system with the Computerized Maintenance Management System (CMMS), checking for historical maintenance, overdue maintenance, certifications, at the same place and time that we are getting real-time operational data and insights. Using real-time data as input, we expect to expand the failure prediction application for other BOP parts (such as regulators, shuttle valves, SPMs (Submounted Plate valves), etc) and increase the applicability for other critical equipment on the rig.
Objectives/Scope The drop on the daily rates for the Drilling Rigs in the recent years has pushed Drilling Contractors in the industry for innovative solutions. Industry 4.0 is bringing many features and technologies to overcome these challenges and help the companies to meet this new scenario. This paper will present how a partnership between Ocyan, an ultra-deep-water Drilling Contractor and RIO Analytics, an A.I. technology company that develops solutions for failure prediction of industrial assets, is using artificial intelligence and Data Analytics to manage and control drill pipes operation and prevent failures, correlating different sources of information. Drill pipe is one of the most critical equipment on a deepwater Drilling Rig and Drill pipes incidents are one of the biggest causes of nonproductive time and unplanned costs in the drilling industry. In most cases, the lack of information about the drill pipes, such as historical and operational efforts related to their individual use make it very hard to investigate an incident that occurred, and consequently, to predict a pipe failure. Also, some operational limits (such as make-up torque and elevator capacity) that are driven by dimensional inspection results are often not used correctly for operational planning, leading to unnecessary risks. Methods, Procedures, Process To be able to apply failure prediction algorithms and correlate operational and historical information for each individual drill pipe, a web-based software was developed building a valuable database and management system, allowing users to easily navigate for drill pipes information, generate reports, and simulate operational scenarios by providing operation planned tally (list of drill pipes). Warnings are generated as the results for the simulations indicating any risk for operations. Critical situations are made available to the rig crew, immediately transmitted to the Ocyan's Decision Support Center (CSD) and management team onshore, while less critical alerts are recorded in the system for further investigation. Software integrates with different inspection reports formats and automatically updates critical information on drill pipe's database, allowing also to identify invalid or wrong information on these reports, upon inspection criteria used. Results, Observations, Conclusions With the implementation of this predictive maintenance solution, companies aim to increase Operational and Process Safety, avoid NPT and reduce maintenance cost regarding the Drill Pipes. Novel/Additive Information Based on the integration with real-time data from rig sensors and identification of active operational tally, it has been possible to automatically control drilled meters and rotating hours for each drill pipe, which triggers inspection requirements, generating automated work orders for the CMMS. Also, an algorithm was developed to calculate real-time damage in each drill pipe during operation, considering the most significant parameters (such as torque, tension, drilling depth, wear, pressure, dog leg severity, jarring, etc.), using it to provide valuable information for failure prediction.
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