Currently, real-time adjustments to drilling parameters such as weight on bit (WOB), drillstring revolutions per minute (RPM), flow rate, etc., are based primarily on experience. This is mainly due to the uncertain nature of information (both downhole and surface) available and inability of humans to aggregate multiple data streams in real-time to make optimal decisions. The objective therefore is to build a decision support tool that can overcome these limitations by automatically aggregating this data, identifying drilling inefficiency and suggesting optimal drilling parameters.

The methodology presented in this paper uses a Bayesian network to represent the drilling process and is capable of representing uncertainty in a way that is robust to bad sensor data. The model is updated in real-time and tracks variations in drilling conditions. Various dysfunctions such as bit balling, bit bounce, whirl, torsional vibrations, high mechanical specific energy (MSE), auto-driller erratic behavior, etc., are identified by tracking the movement characteristics of various sensor data in relation to model predicted values. A holistic drilling optimization index is thus derived by aggregating all this information. This index coupled with the drilling dysfunction prediction ultimately enables recommendation of drilling parameter corrections.

The drilling optimization index has been integrated into a drilling rig data aggregation system currently in operation on twenty rigs in North America. The system has access to real-time data, both at low frequency (less than 1 Hz) as well as data in the 1 to 10 Hz range, and also contextual data (such as data typically available in a tour sheet or well plan). In deploying the system, human factors aspects were given significant consideration. A typical driller is not familiar with concepts such as Bayesian networks, MSE, etc. By displaying the effectiveness of drilling as a single, dimensionless parameter, an index that varies between 0 and 1, with 0 representing inefficient drilling and 1 representing optimal drilling, the message is effectively communicated to the driller. The index is currently depicted in a very intuitive "speedometer" type of visual. Values are low and closer to 0 when dysfunctions occur, and when that happens suggestions are provided on how to mitigate the dysfunctions. These suggestions are visually presented in the form of operational cones in the WOB-RPM space. Additionally, the variation of the index with drilling depth is displayed to enable the driller to identify how formation changes impact drilling performance. This was found to be useful to drilling engineers who are generally tasked with optimizing the drilling process.

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