Introduction of modern computer technologies to drilling control systems opened new opportunities for real-time diagnosis of the drill bit conditions and preventing any further damage. While usage of MWD systems equipped with corresponding downhole sensors significantly increases the efficiency and accuracy of downhole drilling monitoring and diagnosis, there remains a niche in the market for much less expensive drilling monitoring systems based mainly on the surface measurements. These measurements usually include such parameters as static and dynamic hook load, stand pipe pressure measurements, surface torque, differential mud flow rate, and possibly some surface vibration data.

The expert system approach suggested in this paper offers an efficient way of combining some basic measurements provided by the surface sensors for early diagnosis and prevention of possible damage of the downhole drilling equipment, primarily the drill bit itself. This paper addresses both aspects of the proposed methodology – the system configuration and some details of the data analysis algorithms. The fundamental theory behind the proposed approach is based on certain elements of fractal analysis as well as artificial neural networks. A huge amount of data needs to be collected and organized in a database to cover different operational and varying drill bit conditions.

The data analysis is performed for multiple time frames in order to identify both a big picture and more specific details. Application of the fractal performances allows obtaining important practical information during standard field operations, without conducting a special controlled field experiment.

We present some real field data examples used for training our model and assessing the current drill bit conditions by using the proposed methodology. We believe that this methodology opens new opportunities for real-time drilling optimization that can be efficiently implemented within the scope of the existing drilling practice.

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