基于Freeman链码的智能井压力数据处理方法

Intelligent well pressure data analysis approach based on Freeman chain code

智能井通过永久性井下传感器可以获得大量的压力、温度、流量、物性等参数,采集的数据量非常庞大并且非常复杂。有效的数据处理可以提高数据分辨率,增大传感器系统适用性,可以对原始数据进行纠偏和校正,因此数据处理在测量系统中的作用越来越重要。笔者在前人研究的基础之上提出一种基于Freeman方向链码的曲线平滑自适应方法,分别介绍了这种方法的基本思想、算法原理和具体的实现过程,并以模拟实时压力数据进行实验验证。结果证明,该方法是一种简单、快速、有效的数据处理算法,而且相对于同类算法还具有其独特的优势。

Intelligent well can get a lot of pressure, temperature, flow, and other parameters through the permanent downhole sensors, and the data collection is very large and very complex. Efficient data processing can improve the data resolution, increasing the applicability of the sensor system, and correcting the raw data and calibration, thus data processing play increasingly important role in the measurement system. The author on the basis of previous studies on the proposed adaptive method based on curve smoothing direction Freeman chain code,introduced the basic ideas, principles and specific algorithm implementation process of this approach, and simulated real-time pressure data experiments. The results show that this method is a simple, fast and efficient data processing algorithms, but also with respect to the same algorithm also has its unique advantages.