Monitoring of down-hole parameters for early kick detection

The sudden hydrocarbon influx from the formation into the wellbore poses a serious risk to the safety of the well. This sudden influx is termed a kick, which, if not controlled, may lead to a blowout. Therefore, early detection of the kick is crucial to minimize the possibility of a blowout occurrence. There is a high probability of delay in kick detection, apart from other issues when using a kick detection system that is exclusively based on surface monitoring. Down-hole monitoring techniques have a potential to detect a kick at its early stage. Down-hole monitoring could be particularly beneficial when the influx occurs as a result of a lost circulation scenario. In a lost circulation scenario, when the down-hole pressure becomes lower than the formation pore pressure, the formation fluid may starts to enter the wellbore. The lost volume of the drilling fluid is compensated by the formation fluid flowing into the well bore, making it difficult to identify the kick based on pit (mud tank) volume observations at the surface. This experimental study investigates the occurrence of a kick based on relative changes in the mass flow rate, pressure, density, and the conductivity of the fluid in the down-hole. Moreover, the parameters that are most sensitive to formation fluid are identified and a methodology to detect a kick without false alarms is reported. Pressure transmitter, the Coriolis flow and density meter, and the conductivity sensor are employed to observe the deteriorating well conditions in the down-hole. These observations are used to assess the occurrence of a kick and associated blowout risk. Monitoring of multiple down-hole parameters has a potential to improve the accuracy of interpretation related to kick occurrence, reduces the number of false alarms, and provides a broad picture of down-hole conditions. The down-hole monitoring techniques have a potential to reduce the kick detection period. A down-hole assembly of the laboratory scale drilling rig model and kick injection setup were designed, measuring instruments were acquired, a frame was fabricated, and the experimental set-up was assembled and tested. This set-up has the necessary features to evaluate kick events while implementing down-hole monitoring techniques. Various kick events are simulated on the drilling rig model. During the first set of experiments compressed air (which represents the formation fluid) is injected with constant pressure margin. In the second set of experiments the compressed air is injected with another pressure margin. The experiments are repeated with another pump (flow) rate as well. This thesis consists of three main parts. The first part gives the general introduction, motivation, outline of the thesis, and a brief description of influx: its causes, various leading and lagging indicators, and description of the several kick detection systems that are in practice in the industry. The second part describes the design and construction of the laboratory scale down-hole assembly of the drilling rig and kick injection setup, which is used to implement the proposed methodology for early kick detection. The third part discusses the experimental work, describes the methodology for early kick detection, and presents experimental results that show how different influx events affect the mass flow rate, pressure, conductivity, and density of the fluid in the down-hole, and the discussion of the results. The last chapter contains summary of the study and future research.

Availability estimation and management for complex processing systems

“Availability” is the terminology used in asset intensive industries such as petrochemical and hydrocarbons processing to describe the readiness of equipment, systems or plants to perform their designed functions. It is a measure to suggest a facility’s capability of meeting targeted production in a safe working environment. Availability is also vital as it encompasses reliability and maintainability, allowing engineers to manage and operate facilities by focusing on one performance indicator. These benefits make availability a very demanding and highly desired area of interest and research for both industry and academia. In this dissertation, new models, approaches and algorithms have been explored to estimate and manage the availability of complex hydrocarbon processing systems. The risk of equipment failure and its effect on availability is vital in the hydrocarbon industry, and is also explored in this research. The importance of availability encouraged companies to invest in this domain by putting efforts and resources to develop novel techniques for system availability enhancement. Most of the work in this area is focused on individual equipment compared to facility or system level availability assessment and management. This research is focused on developing an new systematic methods to estimate system availability. The main focus areas in this research are to address availability estimation and management through physical asset management, risk-based availability estimation strategies, availability and safety using a failure assessment framework, and availability enhancement using early equipment fault detection and maintenance scheduling optimization.

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.