Detection of Symptoms for Revealing Causes Leading to Drilling Failures

When a diagnosis of a problem is known, the problem can usually be solved efficiently. This paper presents a method that helps reveal the most probable cause of a drilling-process failure immediately after occurrence. Normally, it takes some time to investigate and evaluate all available information before the correct cause can be determined. The method presented is targeted at reducing this time and at the same time improving the quality of the interpretation. The method relies on input parameters from the ongoing drilling process, parameters that behave irregularly and are referred to as symptoms or errors when the irregularity is severe. Recognized symptoms are used as input parameters in a knowledge-modeling method that relates symptoms to failures. The method was verified on the basis of historical drilling data, and its ability to point out the correct causes with high probability was demonstrated. Field testing the method is yet to be performed.