Objectives/Scope: Traditionally Daily Drilling Reports were used as the main source of the data input for Invisible Lost Time calculations with great success. One observed drawback was that the hidden causes that lead to invisible lost time were not identified. This paper outlines the framework to incorporate Real-Time high frequency rig-floor sensor data in the invisible lost time calculation, identification, and reduction exercises. This approach breaks down, downhole activities into smaller, measurable, discrete sub-activities that are measured, benchmarked, and targeted for improvement.

Methods, Procedures, Process: This paper covers three major downhole activities: Drilling, Tripping, and Running Casing, breaking them down into thirty-three sub-activities. A three-year drilling activities dataset is categorized into comparable groups based on drilling rig capabilities and formation characteristics. The sub-activities are then presented on a histogram with the average, P10, P50, and P95 values of each sub-activity determined based on a pre-accepted category. This benchmarking process enabled the generation of the targets that are currently being used for calculating and understanding the invisible lost time causes in the operation.

Results, Observations, Conclusions: Previous reporting would simply identify the phase of operation that is creating the invisible lost time. Complementing the existing Invisible Lost Time calculation model with Real-Time data has enabled the understanding of the operational steps that lead to sub-optimal performance. A good example of this would be the drilling phase. The new approach is able to pinpoint the steps in the drilling phase that generates the invisible lost time, for example when post-connection times are excessively exceeding historical norms. Another advantage is the ability to run the report in the middle of the activity and highlight that the current rate of execution is not optimal. This enables the supervisor to investigate the situation and propose solutions to improve performance on the ongoing activity. In addition, this approach has been used successfully for ranking the performance of casing running service companies and helping in the decision-making process for awarding new contracts.