A Class Based Approach for Utilizing Existing Data to Demonstrate Compliance and Equipment Availability

Abstract

Modern drilling systems have a significant number of sensors already integrated into their design. However, this data is generally underutilized with respect to maintaining class notations or periodic survey activities. Analyzing existing data can yield reductions in non-productive time (NPT) in an era where efficiency is the key to success in offshore exploration and production. A Joint Development Project (JDP) with class, rig operator and equipment vendors was established to understand how data analytics may support class requirements while also increasing availability and reliability through condition based monitoring. This paper describes the approach taken in the JDP and the main findings from this real-world example of data analytics applied to current sensor and logging data Methods, Procedures, Process: The JDP looked to utilize data from 15,000 sensors onboard a drill ship which generated 36 billion data points from the vessels. The sensor data was combined with unstructured data from operational logs and maintenance records to find ways to demonstrate compliance to class and regulatory requirements via digital techniques. Ultimately this approach could remove the need for calendar based inspections while optimizing preventative maintenance tasks and frequencies, both of which increase equipment availability of drilling systems. The JDP resulted in several significant deliverables. The first is a draft assurance framework as to how data streams can be used by drilling units to support classification requirements without the need for intrusive inspections. The second delivery was a data quality assessment report to demonstrate how data quality can be measured and ensure that there is high confidence in alternative compliance approaches. The third delivery was an optimized preventative maintenance based on analytics of the existing data streams. The fourth deliverable involved creating "Big Data" analytic models (i.e., machine learning) for anomaly detection and failure mitigation. The JDP demonstrated that data analytics on existing data streams can be used to demonstrate compliance with classification survey requirements and increase availability. An assurance process for data quality creates trust in the analytics before important decisions about inspections and maintenance are made. This is especially true for safety critical systems where machine learning models may be used to mitigate and address reliability issues. This paper describes the approach taken in the JDP and the main findings from this real-world example of data analytics applied to current sensor and logging data. The project's major novel development issues were the development of an assurance framework, data quality assessment framework and an approach on how sensor based information can be used in this context classification. The use and treatment of sensor data in general is on the exploratory level and leading edge of such applications in the industry.