Processing And Analysis Of Long-term Downhole Pressure Data From Permanent Down-hole Gauges

Smart well technology is an efficient management methodology of well production in recent years. It acquires downhole pressure, temperature and rate data from permanent downhole gauges. Such data will be proceed and analyzed and the production strategy will be developed. Then the strategy will be feedbacked to downhole equipment to control the production in real time. The large amounts of data gathered from permanent down-hole gauges provide key information for engineers to monitor well/reservoir perfromance and develop measurement. The data are gathered under unattended situation and usually include some outliers and noises. Additionally, the reservoir properties may change over production time. So it’s very important to develop an efficient method to process and analyze the long-term data from smart well.A series of synthetic long-term downhole pressure data are used to develop the processing and interpretation methodology in this thesis. As for the outlier and noise data, some methods, such as wavelet analysis, lowpass filtering, Savitzky Golay filtering and Pavel Holoborodko filtering, were used to process such data and the best method was determined basing on their performance. The methods for filtering abnormal behavior and reconstructing flow rate are studied with nonlinear regression and area method. Considering the variation of reservoir properties with time, the deconvolution method and moving window analysis method are integrated to develop an interpretation method for long-term downhole pressure data in this thesis. A computer program for processing and interpretation of long-term downhole pressure data has been developed with VB.Net and Matlab. A set of measured data from one offshore oil well was used to validate the methodology developed in this thesis. The results show that the developed methodology is robust to outliers and noises. The interpretation result is better to reflect the variation of reservoir properties with time when comparing with traditional methodology. The developed methodology provides a technical support for the data application and production strategy decision of smart well.