State of the art drilling operations analysis is mostly dependent on conventional daily activity reporting. However, these activity reports are based on human observations and judgment. This fact implies a number of limitations such as the coarse level of detail and subjective coding systems. To overcome these problems a rule-based system has been applied to autonomously analyze real time surface sensor data. The system evaluates the sensor data stream and acquires crucial process information as a basis for further analysis. Scope of the system is the recognition of drilling operations, such as tripping, making connections, reaming, washing etc. to extend and enhance standard reporting. This way a standardized and objective categorization of the drilling process can be achieved at a level of accuracy and detail not reached so far.

Another benefit is the automated reporting feature. By the recognition of the rigs current state, the system is able to propose an impartial process description. This automatism leads to a reduction of the time spent on reporting and leaves more time to focus on unexpected events and lessons learned. Analysis of field data allowed introducing new key performance indicators (e.g. wellbore treatment time per depth interval) for benchmarking, which are determined automatically during the evaluation process. This type of benchmarking does not rely on company specific activity coding systems. This way cost and time-consuming data management effort e.g. to compare operated and non-operated wells are eliminated.

The new system was applied to wells drilled in the Vienna Basin during the past year. As a conclusion it can be stated that the application of this system significantly improves the accuracy and resolution of the drilling process description reducing data management effort. The objective categorization of process information is a key enabler for benchmarking specifically when identifying hidden lost time.

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