Methodological And Experimental Studies Of Leak Monitoring For Long-Distance Oil & Gas Pipeline--Transient Identification

Pipeline accidents and subsequent leaks cause huge economic loss, and environmental pollutions that can even endanger life safety. Economics, safety, and environmental issues associated with pipeline leaks are a growing concern around the world. Detection of hydrocarbon leaks from deepwater sub-sea pipelines represents a special challenge to both the oil and gas industry and regulators. Leaks in pipelines may be ascribed to several factors, such as corrosion, cracks, and defects in the pipe joints. Persistent but small leaks are difficult to detect even in suitable ambient conditions.The effectiveness of a method in detecting the onset of a leak can be measured by several attributes: accuracy, sensitivity, reliability, and robustness. The main task of this research work focuses on how to effective the Computer Pipeline Monitoring (CPM) and leak detection while the pressure and flow rate at inlet and outlet are measured.In section 1, the review of a great number of papers on pipeline leak detection shows model-based monitoring schemes have a great potential in detection and localization of leaks in pipelines, while the remoteness of the deepwater flow-line coupled with a number of complex interactions between the released fluids and the deepwater environment effectively eliminates most external detection methods. As a transient inverse proposition of pipeline flow dynamics system identification, existent problems applied to CPM based on Real Time Transient Method (RTTM) are pointed out.Next, the fundamentals of pipe fluid transient models are introduced considering gas, oil, and water has different compressibility, viscosity, and time-variant property.In section 3, an innovation based adaptive detection algorithm is applied to filter noise in observed series. A Uniformly Most Powerful Test (UMPT) is applied to the composite hypothesis testing of leak signal with little prior statistical knowledge. Numerical experiments and citation examples show that UMPT is sensitive to leak detect and whether the process of pipeline flow is stationary or not. The false alarm rate is lower than 5% with the detect tune being shorten. This is compared to the Sequential Probability Ratio Test (SPRT) method, which needs to estimate leak signal amplitude at first, but has better practicability according to famous LDS software claimed in reference.The improvement of the CM by coupling with the EKF is discussed in details. It is a better identifier of dynamics system because EKF has a time variant track under non-stationary operation condition. A leak position estimation formula was presented based on the time-meaning Darcy-Weisbach (DW) equation to improve the relative precision of leak location. The results of test data in a weak non-stationary situation indicate that the presentedprocedure is sensitive to small leak flow rate as 1% pipe inlet flow rate with 2.41% average relatively error of position estimation.Section 5 further presents a more complex but effective procedure, named Distributed Detection and Decision Fusion based on Generally Likelihood Ratio Test, D-GLRT. Detection and decision fusion is used to get desirable sensitivity with low false alarm rate under strong non-stationary operation conditions such as transient gas-condensate two-phase flow and inaccuracy flow measurement. Field data was taken from sub-sea gas and oil pipelines one 310km long another 365km long.Also, the performance of these three procedures over all test data of the hydraulic model experiment is investigated. In non-stationary conditions with the given false alarm rate 5%, the minimum detectable is 0.8% with the relative position estimated error below 4.32% for test water pipe. It is confirmation that the three methods presented in this dissertation are effective, and reliable after experiencing simulation example test data and field data.Last, the main contributions of the dissertation are summarized and the further work is suggested.The China National Offshore Oil Corporation Research Centre financially supports the subject call...