Operations Efficiency: Improved Well Planning Methodology Based on Invisible Lost Time Smart KPIs

Abstract To optimize drilling operations efficiency and improve well planning methodology, Kuwait Oil Company (KOC) has capitalized on the existence of the real-time drilling decision center (RTDDC) to scrutinize the breakdown of rig operations, detect invisible lost time, and explore measures to address the root causes of this lost time. Advanced data analytics were applied to real-time data to investigate the impact of invisible lost time on drilling operations and use it to benchmark rig performance. This information will now be used for better well planning and improved performance tracking, which would traditionally be hard to achieve using legacy methods such as daily drilling reports and spreadsheet reporting. The analysis started with combining real-time data with rig daily drilling report data to perform quality control and validate the operational phases. The combined data output was used to calculate rig states within the different operational intervals throughout the lifetime of the well. Consequently, smart key performance indicators (KPIs) were calculated to map previously identified invisible lost time within the different operation phases. Such KPIs were then normalized with respect to a single frame of reference to establish the basis for comparison within a drilling campaign or across a rig fleet. Finally, the normalized KPIs were analyzed to produce a best composite matrix indicating the best performance achieved in the different operation phases to enable more accurate well planning. This methodology was applied to two wells. From the results, a best performance analysis was conducted based on a fair comparison between the rigs/wells. Operational inefficiencies were accurately captured, and root-cause analysis was effectively categorized in terms of crew performance, flat-time operations, and tool performance. All the results were translated into a framework that enables an enhanced well planning process and an improved performance tracking methodology. This study exemplifies how to apply advanced data analytics on high-frequency and low-frequency drilling data within a drilling project or across different rigs to identify best potential performance and how to set a benchmark, translate it into goals, and thereby improve the operational efficiency of the project/business and deliver cost savings.