Spears and Associates (2009) estimated spending on drilling and completions at over USD 250 billion in 2008. With rig costs estimated to consume 37% or USD 92.5 billion of that spending, every effort to reduce drilling time has a direct impact on our bottom line. Estimates of non-productive time (NPT) ran from 15–40% or USD 14–37 billion, depending on well type and operator. The causes were varied and included technical and non-technical challenges such as wellbore stability, stuck pipe, weather, logistics, etc. Obviously any effort made to reduce NPT will tremendously impact bottom line spending.

Given that causes of drilling inefficiencies are often known and predictable, why do we struggle to improve our results? The challenge is often one of fundamental knowledge management, which includes: difficulty in using historical data, lack of access to relevant data, and the inability to effectively correlate multiple wells in a single view.

Imagine how much more efficient the drilling process would be if you could plan future wells based on nearby offset wells populated with important drilling knowledge from a living drilling knowledge base. The knowledge base would contain all surface drilling parameter data, BHAs, bit records, MWD/LWD data, drilling events, lessons learned, best practices, etc., associated with each well. Data from new wells added to the knowledge base in real time make it more robust, allowing for tracking of position with respect to the shared earth model and updating of the model while drilling. All pertinent data could be displayed side-by-side, allowing correlation of multiple wells by formation, depth or time, in one, two, or three dimensions. Comparison of multiple wells in a single view facilitates better well planning through anticipation of problems and mitigation of risks. This comparative ability leads to an increase in drilling efficiency and a decrease in rig time, which can result in reduced spending.

This paper illustrates techniques for improving collaboration and analysis of real-time and historical drilling data, increasing the cost effectiveness of drilling efforts, and presents a case study highlighting the achievable benefits.

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