Key points are not available for this paper at this time.
Abstract Abnormal surface torque and hook load values are symptoms of downhole drilling condition deterioration which can result in unexpected situations. Usually, friction tests are performed at regular intervals and rig personnel uses these measurements to monitor trend variations in order to detect possible risk of poor hole cleaning or increased borehole tortuousity. The quality of the detection can vary greatly in function of the work load and experience of the drilling staff. The availability of real-time measurements through data servers make it possible to automate and systemize the monitoring process and therefore trigger alarms before drilling problems really occurs. This paper presents a computer system used to systematically analyse real-time data in order to monitor downhole conditions. Such a system can utilize much more data than just the above mentioned friction tests, because mechanical, hydraulic and temperature models can calculate predicted hook load and surface torque in any drilling conditions. The numerical models are automatically calibrated (adjustment of drill-pipe linear weight, factors for mechanical and hydraulic friction and heat generation). The evolution of proper calibration factors is used to detect poor downhole conditions. Automatically generated messages are sent to key personnel who can evaluate the potential problems and take necessary actions. To validate this new methodology, the system has been run on recorded data from three wells on an oil field in the North Sea. A data filtering technique has been developed and applied to solve problems with noisy and erratic real-time signals. With correct input parameters, the system has clearly indicated unexpected measurements several hours before a pack-off problem occurred and therefore proven that the methodology could help in detecting the worsening of downhole drilling conditions. Availability of large amount of real-time data at the rig site or in onshore drilling centres does not necessarily facilitate the recognition of drilling problems. However, online interpretation systems, as the one described above, can systematically analyze the logged data to detect as early as possible the deterioration of hole conditions during drilling operations and corrective actions can be taken before any major problem has really occurred.
Cayeux et al. (Tue,) studied this question.