A digital twin development framework for fatigue failure prognosis of a vertical oil well drill string

This thesis presents a novel methodology for fatigue life prognosis of vertical oil well drill strings through the development of a digital twin frame work. A technique is proposed to classify vibration types with their severities and estimate the remaining useful life time of the drill string based on various indirect measurements made at the surface level. The classification was done using a machine learning algorithm developed based on a Hidden Markov Model HMM). Training data for the algorithm were generated using a bond graph simulation of a vertical drill string. A three-dimensional lumped segment bond graph element and an interface element available in the literature were used to develop the simulation. The bond graph elements are developed based on a Newton-Eular formulation and body-fixed coordinates. The simulation was upgraded by introducing a fluid drag model and refining it with accurate element compliance values. Non linear fluid drag force statistical models were developed through the design of experiments(DoE) approach considering the non-linear geometry of the drill pipes,the drilling fluid rheology, and fluid velocity. A series of fluid-structure interaction(FSI) simulations were employed to develop the statistical models for the lateral vibration damping and the axial drag force dueto the drilling fluid flow through the pipe and the annular space. An apparatus was designed and fabricated to verify the FSI simulation. Further, a method was introduced to accurately determine the axial, shear, bending, and torsional compliances of geometrically-complex drill string segments represented by the bond graph elements. The trained HMM-based classifier using bond graph-generated training data selects the appropriate parameter set for the same bond graph to generate stress history for fatigue life prognosis. A generalized fatigue life estimation method was developed using SalomeMecaᵀᴹ, an open-source finite element analysis code. A detailed workflow for multi-axial, non-proportional, and variable amplitude (MNV) fatigue analysisis also provided. Three case studies are presented to demonstrate the significance of the nonlinear fluid drag models, the fatigue prognosis framework, and the digital twin development framework. In the first case study, the bond graph with the developed drag models showed higher stress fluctuations at the drill pipe threaded connection than the one with a static model. The second case study demonstrated the function of the proposed fatigue life prognosis framework as an optimization tool. In the case study, the optimum placement of the stabilizers reduced the drill collar damage by 66% compared to the worst-case scenario. The third case study used a laboratory-scale vertical drill string vibration simulator apparatus designed and fabricated to implement the framework as a proof of concept. It demonstrated the potential to use surface measurements to classify the vibration type and its severity for fatigue life prognosis

Process fault prediction and prognosis based on a hybrid technique

The present study introduces a novel hybrid methodology for fault detection and diagnosis (FDD) and fault prediction and prognosis (FPP). The hybrid methodology combines both data-driven and process knowledge driven techniques. The Hidden Markov Model (HMM) and the auxiliary codes detect and predict the abnormalities based on process history while the Bayesian Network (BN) diagnoses the root cause of the fault based on process knowledge. In the first step, the system performance is evaluated for fault detection and diagnosis and in the second step, prediction and prognosis are evaluated. In both cases, an HMM trained with Normal Operating Condition data is used to determine the log-likelihoods (LL) of each process history data string. It is then used to develop the Conditional Probability Tables of BN while the structure of BN is developed based on process knowledge. Abnormal behaviour of the system is identified through HMM. The time of detection of an abnormality, respective LL value, and the probabilities of being in the process condition at the time of detection are used to generate the likelihood evidence to BN. The updated BN is then used to diagnose the root cause by considering the respective changes of the probabilities. Performance of the new technique is validated with published data of Tennessee Eastman Process. Eight of the ten selected faults were successfully detected and diagnosed. The same set of faults were predicted and prognosed accurately at different levels of maximum added noise