Model-Based Diagnosis of Drilling Incidents
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Oil and gas drilling is an advanced process with very little instrumentation, where drilling uid is transported through rotating drillstrings of up to several kilometers, possibly at extreme depths with high pressure and temperature. A drilling bit is used at the bottom of the drillstring to crush the formation, and the drilling uid is used to carry the cuttings to the surface, as well as maintain the pressure in the well. Drilling is a costly operation, especially o shore. Incidents can occur that may slow down the progress. Detecting such incidents manually, especially those occurring down in the well, may be di cult. Early symptoms may give small variations in pressure, temperature, and ow rates, possibly covered in measurement noise. The push for drilling more complex wells in more remote locations demands more from the drilling control and monitoring system. With advances in drilling control technology such as managed pressure drilling, and sensor technology such as wired drill pipe, the complexity of the control system greatly increases. With a high data rate of sensor readings, as well as lower operation margins, an e cient automatic diagnosis system is instrumental in reducing operational delays. This thesis presents di erent model-based methods for achieving early diagnosis of di erent drilling incidents, possibly distinguished from sensor bias, and with estimation of the incident magnitude. The model-based diagnosis system consists of two parts; rst some residuals are generated using either adaptive observers or analytical redundancy relations, then changes to these residuals are detected using a statistical change detection algorithm, required due to measurement noise. Univariate and multivariate generalized likelihood ratio tests are applied, using the probability density function that best matches the noise of the residuals. The thresholds are found using the probability distribution of the test statistic, determined by a speci ed probability of false alarms. The probability of fault detection is also found as a function of the threshold, where data during the incidents are available. Data from a medium-scale ow loop is used to test the diagnosis method, where the noise of the residuals ts the t-distribution well. A multivariate change detection method considering multiple residuals jointly is found to be superior over a univariate method considering each residual separately, and is used to detect and isolate the di erent incidents occurring in the test data. Furthermore, the t- distribution is shown to give an increased probability of detection compared with assuming the more common Gaussian distribution. Simulation of a drilling incident in the high- delity multi-phase simulator OLGA with Gaussian noise in the measurements is also considered. The diagnosis framework proposed in this thesis is module-based, where the methods in each module are simple enough to be implemented in drilling monitoring software at the rig, and can be run in real-time. However, a limitation with the proposed method is that good data during the normal operating mode is required for reliable detection and isolation. Future work and implementations should take this into account, and facilitate automatic acquisition of new data when changes to the process are made.