E3S Web Conf.
Volume 206, 20202020 2nd International Conference on Geoscience and Environmental Chemistry (ICGEC 2020)
|Number of page(s)||4|
|Section||Earth Geological Energy Mining And Landform Protection|
|Published online||11 November 2020|
Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges
1 State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China; email@example.com (F.X.); firstname.lastname@example.org (L.S.)
2 Tianjin Research Institute of Water Transport Engineering, Tianjin 300000, China; email@example.com
3 CNPC Bohai Drilling Engineering Company Ltd., Tianjin 300457, China; firstname.lastname@example.org
* Corresponding author: email@example.com firstname.lastname@example.org
During petroleum exploration and exploitation, the oil well-testing data collected by pressure gauges are used for monitoring the well condition and recording the reservoir performance. However, due to the large number of the collected data, the classification of this large volume of data requires a previous processing for the removal of noise and outliers. It is impractical to partition and process these data manually. Vibration-based features reflect geological properties and offer a promising option to fulfil such requirements. Based on the 75 on-site measured samples, the time-frequency-domain features are extracted and the classification performance of three classical classifiers are investigated. Then the downhole data processing and classification method is present by analysing the cross interaction of different types of data features and different classification mechanism. Several feature combinations are tested to establish a processing flow that can efficiently remove the noise and preserve the shape of curves, high signal to noise ratio rates, with minimum absolute errors. The results show that optimal multi-feature combination can achieve the highest working stage identification rate of 72%, the parameters optimized support vector machine can achieve the better classification performance than other listed classifiers. This paper provides a theoretical study for the data denoising and processing to enhance the working stage classification accuracy.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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