Spectral power ratio method and system for detecting drill bit failure and signaling surface operator

#The present invention relates to systems, methods, and subassemblies for drilling oil, gas, and analogous wells, and more particularly to downhole failure detection.

#When drilling a well it is desirable to drill as long as possible without wearing the bit to the point of catastrophic bit failure. Optimum bit use occurs when a bit is worn sufficiently that the useful life of the bit has been expended, but the wear is not so extensive that there is a high likelihood of mechanical failure which might result in leaving a portion of the bit in the well. Poor drilling performance, increased BHA (Bottom Hole Assembly) wear, and more frequent fishing jobs all result from continued drilling with bits which are in the process of mechanical failure. A system capable of detecting the early stages of bit failure, with the additional capability of warning the operator at the surface, would be of great value solving the problem of drilling to the point of catastrophic bit failure.

#The innovations in this application provide a reliable, inexpensive means of early detection and operator warning when there is a roller cone drill bit failure. This system is technically and economically suitable for use in low cost rotary land rig drilling operations as well as high-end offshore drilling. The solution is able to detect impending bit failure prior to catastrophic damage to the bit, but well after the majority of the bit life is expended. In addition to failure detection, the innovative system is able to alert the operator at the surface once an impending bit failure is detected.

#The disclosed inventions will be described with reference to the accompanying drawings, which show important sample embodiments of the invention and which are incorporated in the specification hereof by reference, wherein:

#FIG. 1 shows the sensor placement relative to the bit.

#FIG. 2 shows a process flow for the spectral power ratio analysis method.

#The numerous innovative teachings of the present application will be described with particular reference to the presently preferred embodiment (by way of example, and not of limitation).

#Further Background: Adaptive Filters (Neural Networks)

#A neural network can be generally described as a very flexible nonlinear multiple input, multiple output mathematical function which can be adjusted or “tuned” in an organized fashion to emulate a system or process for which an input/output relationship exists. For a given set of input/output data, a neural network is “trained” until a particular input produces a desired output which matches the response of the system which is being modeled. After a network is trained, inputs which we not present in the training data set will produce network outputs which closely match the corresponding outputs of the actual system under the same inputs. FIG. 52 illustrates the process.