August 31, 2021 | 9:00 a.m. - 9:30 a.m. CDT (UTC-05:00)

During well construction, every activity is carefully recorded by the onboard crew, and this produces a large amount of textual descriptions in the daily rig reports. Later, these descriptions can be used to detect and calculate Invisible Lost Time for specific rig activities.

To perform the ILT analysis, these manual descriptions must be checked by a SME who correctly classifies the operation type. Manual classification consumes a lot of time and it might be done by multiple people, leading to biases caused by subjective interpretations. To overcome this problem, we have developed an Artificial Intelligence driven solution based on Natural Language Processing.

The business analytics dashboard supported by NLP and clustering machine learning enables the user to quantify the impact of ILT in the operation and understand various scenarios that cause high NPTs. This will help in defining better optimizing strategies for rigs and well equipment usage during the well campaign. In this webinar, we showcase this data analytics-based approach for investigation of ILT in textual reports. This approach uses NLP and clustering techniques for creating integrated workflows for an effective, data-driven decision making.