Vibration-based Processing and Classification Method for Oil Well-testing Data from Downhole Pressure Gauges

Feng Xin,Liu Shugui,Li Shaohui,Feng Qiang,J Sun,M Nawaz,X Liu

doi:10.1051/e3sconf/202020601024

Abstract

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.

Highlights

Long-term downhole pressure monitoring is playing an important role in improving reservoir management and workflow optimization
What’s more, in order to obtain the best parameters c and g, we introduce particle swarm algorithm (PSO) to avoid the occurrence of overlearning and underfitting states
To improve the final classification performance, firstly, the original downhole pressure data are processed by wavelet based denoising method

Summary

Introduction

Long-term downhole pressure monitoring is playing an important role in improving reservoir management and workflow optimization. With the deepening of oil well exploration and development, the downhole formation structure is becoming more complex, the types of the difficult operating wells is enriching, including the deep and ultra-deep wells, the high temperature and high pressure wells, and the high-angle horizontal well. Such exploration faces the problems with noise, attenuation and nonlinear distortion [1]. The smart well technology is becoming an important methodology to study the downhole well-testing data processing and the data interpretation [2]. Many published papers described the methods of processing and interpreting pressure data

Objectives

Methods

Results

Conclusion