Implementation of noise filtering algorithm for automated pressure transient analysis

Coşar, Doğuhan
In order to estimate reservoir parameters such as permeability and skin factor, well tests are performed. Pressures are recorded periodically during these tests. These parameters are crucial in predicting the amount of oil or gas that can be produced. The analysis of pressure changes provide information about the reservoir characteristics and its ability to produce hydrocarbons at a certain amount of time. Three different cases of drawdown tests are analyzed. Derivative curves are plotted using data points that are separated by certain intervals, ranging from 0.10 to 0.50 of a log cycle, because using adjacent data points results in very noisy derivative values while differentiating. Forty one different differentiation intervals are used in total. A new method is developed to find the log cycle interval that eliminates the highest level of noise without overly flattening the derivative curve. Second and third derivatives are also calculated for each and every log cycle interval and checked in order to find the most suitable one. Early, middle and late time regions, hence different flow regimes, of both cases are identified using this log cycle interval. Pressure and time data from middle time region are selected and used for the estimation process since best estimates of permeability can be made from this region. Skin factor and permeability values for both cases are estimated with two different methods. The first one is an iterative method and takes around 1-5 minutes to run while the second one is a graphical method and takes just seconds. Both methods can estimate permeability and skin factor with high accuracy and low error. Negligible amount of difference occurs between the two methods, the first method being slightly more accurate. The results obtained are compared with the results from KAPPA’s Saphir Module, an industry standard PTA module and it is seen that the Python code can make more accurate estimations of permeability and skin factor. Also, it can handle noises up to ±2.5%.


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Citation Formats
D. Coşar, “Implementation of noise filtering algorithm for automated pressure transient analysis,” M.S. - Master of Science, Middle East Technical University, 2022.