Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

Downhole abnormal incidents during oil and gas drilling cause costly delays, and may also potentially lead to dangerous scenarios. Different incidents will cause changes to different parts of the physics of the process. Estimating the changes in physical parameters, and correlating these with changes expected from various defects, can be used to diagnose faults while in development. This paper shows how estimated friction parameters and flow rates can be used to detect and isolate the type of incident, as well as isolating the position of a defect. Estimates are shown to be subjected to non-Gaussian, $t$-distributed noise, and a dedicated multivariate statistical change detection approach is used that detects and isolates faults by detecting simultaneous changes in estimated parameters and flow rates. The properties of the multivariate diagnosis method are analyzed, and it is shown how detection and false alarm probabilities are assessed and optimized using data-based learning to obtain thresholds for hypothesis testing. Data from a 1400 m horizontal flow loop is used to test the method, and successful diagnosis of the incidents drillstring washout (pipe leakage), lost circulation, gas influx, and drill bit nozzle plugging are demonstrated

Drillstring Washout Diagnosis Using Friction Estimation and Statistical Change Detection

In oil and gas drilling, corrosion or tensile stress can give small holes in the drillstring, which can cause leakage and prevent sufficient flow of drilling fluid. If such \emph{washout} remains undetected and develops, the consequence can be a complete twist-off of the drillstring. Aiming at early washout diagnosis, this paper employs an adaptive observer to estimate friction parameters in the nonlinear process. Non-Gaussian noise is a nuisance in the parameter estimates, and dedicated generalized likelihood tests are developed to make efficient washout detection with the multivariate $t$-distribution encountered in data. Change detection methods are developed using logged sensor data from a horizontal 1400 m managed pressure drilling test rig. Detection scheme design is conducted using probabilities for false alarm and detection to determine thresholds in hypothesis tests. A multivariate approach is demonstrated to have superior diagnostic properties and is able to diagnose a washout at very low levels. The paper demonstrates the feasibility of fault diagnosis technology in oil and gas drilling

Incident detection and isolation in drilling using analytical redundancy relations

Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian - they follow a multivariate $t$-distribution - hence, a dedicated generalized likelihood ratio test is applied for change detection. Data from a 1400 meter horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability

Model-Based Diagnosis of Drilling Incidents

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

Learner differences in the online context: Introducing a new method

The paper introduces an alternative method to analyze different learning styles among students. This method was developed as an alternative to more traditional methods such as hierarchical cluster analysis. The method was tested using a large data set (n = 868) which included participants completing a small e-module in addition to a small number of measures to assess learner characteristics. The resulting log files were analyzed using the new method. Results were similar to those observed using traditional methods. The method provides a new starting point for subsequent analysis and identification of learner differences using other information such as log files from e-learning and Massive Online Open Courses (MOOCs)

Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

Downhole abnormal incidents during oil and gas drilling cause costly delays, and may also potentially lead to dangerous scenarios. Different incidents will cause changes to different parts of the physics of the process. Estimating the changes in physical parameters, and correlating these with changes expected from various defects, can be used to diagnose faults while in development. This paper shows how estimated friction parameters and flow rates can be used to detect and isolate the type of incident, as well as isolating the position of a defect. Estimates are shown to be subjected to non-Gaussian, $t$-distributed noise, and a dedicated multivariate statistical change detection approach is used that detects and isolates faults by detecting simultaneous changes in estimated parameters and flow rates. The properties of the multivariate diagnosis method are analyzed, and it is shown how detection and false alarm probabilities are assessed and optimized using data-based learning to obtain thresholds for hypothesis testing. Data from a 1400 m horizontal flow loop is used to test the method, and successful diagnosis of the incidents drillstring washout (pipe leakage), lost circulation, gas influx, and drill bit nozzle plugging are demonstrated

A Framework for Fault Diagnosis in Managed Pressure Drilling Applied to Flow-Loop Data

Data from a medium-scale horizontal flow loop test facility is used to test fault diagnosis in managed pressure drilling. The faults are downhole incidents such as formation influx, fluid loss, drillstring washout, pack-off, and plugging of the drill bit, which are important to detect and handle in order to avoid downtime and possibly dangerous situations. In this paper a fault diagnosis scheme based on an adaptive observer and the generalized likelihood ratio test is applied on the experimental data. The different types of faults are detected and their location are isolated using friction parameter estimates. Results indicate that the method can in most cases identify the type of fault, whereas the location is sometimes more uncertain

Short-term production optimization of offshore oil and gas production using nonlinear model predictive control

The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools. The case-study first looks into the use of NMPC for ‘conventional’ pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the ‘slugging’ phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed