Intelligent identification and time-efficiency analysis of drilling operation conditions

Currently, the operation condition identification and time-efficiency analysis for drilling are mostly dependent on the data transfer efficiency of on-site devices and empirical diagnosis of engineering operators, which suffer from the incapability of handling massive real-time operation data, slow decision making-feedback mechanism, and low prediction accuracy. To efficiently assist the optimization of decision-making of engineering personnel using mud logging data, the data transfer module based on the WITS and WITSML standards was developed, the algorithm integrating the threshold method and neural network method was constructed,the historic data sheet including wellsite information and operation condition identification results of an individual well was tabulated, and the time-efficiency analysis software based on mud logging historic data was programmed. The research showed that the intelligent drilling operation condition identification results of the case-study well are consistent with the actual operation conditions, with the prediction accuracy over 90% and calculation error less than 1% for the drilling time-efficiency, and the application performance is highly satisfactory. This research effectively improves the efficiency of identifying drilling operation conditions and analyzing drilling time-efficiency, and provides guidance for drilling practice.