Abstract

In recent years, the potential value of real-time drilling data has been well documented and applied across the globe within the Oil & Gas Industry. The information is traditionally used for timely and effective decision-making for drilling operations. Real-Time work processes has helped the industry by saving millions of dollars and mitigating the occurrence of critical hole problems. Drilling departments capitalize on the benefits of real-time data even in the planning stages of a well in order to prepare detailed drilling programs based on the history of any nearby wells. The easy access of real-time active and historical data assists the avoidance of incidents based on the witnessed reaction of the formation during the drilling process.

In order to do more with the data obtained, further analysis is required on the surface data to calculate the witnessed performance for certain activities on the rig site. The first step is to analyze surface data to prepare an operational recognition system. To automatically recognize drilling activities, an algorithm was developed that uses therealtime surface parameters data to calculate the current operation state, this is based on a 2-level activity classification hierarchy. The second step is then to use the results of the operational recognition system to determine key performance indictors based on the witnessed operations. It is therefore key that both calculated methods are as accurate as possible.

This paper will describe the business justification and the process formulated to generate meaningful KPI's for Saudi Aramco to allow for benchmarking and the sharing of best practices.

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