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Title: Processing and analysis of transient data from permanent down-hole gauges (PDG)
Author: Li, Xiaogang
ISNI:       0000 0004 2679 8911
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2009
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The Permanent Downhole Gauge (PDG) can monitor the reservoir in real time over a long period of time. This produces a huge amount of real time data which can potentially provide more information about wells and reservoirs. However, processing large numbers of data and extracting useful information from these data brings new challenges for industry and engineers. A new workflow for processing the PDG data is proposed in this study. The new approach processes PDG data from the view of gauge, well and reservoir. The gauge information is first filtered with data preprocessing and outlier removal. Then, the well event is identified using an improved wavelet approach. The further processing step of data denoise and data reduction is carried out before analyzing the reservoir information. The accurate production history is very essential for data analysis. However, the accurate production rate is hard to be acquired. Therefore, a new approach is created to recover flow rate history from the accumulated production and PDG pressure data. This new approach is based on the theory that the relation between production rate and the amplitude of detail coefficient are in direct proportion after wavelet transform. With accurate pressure and rate data, traditional well testing is applied to analyze the PDG pressure data to get dynamic reservoir parameters. The numerical well testing approach is also carried out to analyze more complex reservoir model with a new toolbox. However, these two approaches all suffer from the nonlinear problem of PDG pressure. So, a dynamic forward modelling approach is proposed to analyze PDG pressure data. The new approach uses the deconvolution method to diagnose the linear region in the nonlinear system. The nonlinear system can be divided into different linear systems which can be analyzed with the numerical well testing approach. Finally, a toolbox which includes a PDG data processing module and PDG data analysis module is designed with Matlab.
Supervisor: Zheng, Shiyi Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available