Optimization of the Oil Drilling Monitoring System Based on the Multisensor Image Fusion Algorithm

Abstract

The working environment of the oil drilling platform is harsh, with many uncertain factors and high operating risks. During the drilling process, due to sudden formation factors or improper process operations, it is extremely easy to cause well wall instability, sticking, lost circulation, well kick, and blowout. In addition, other complicated situations and accidents have brought major challenges to drilling safety. In order to improve the technical level of oil and gas exploration and development and achieve the goal of reducing costs and increasing efficiency, it is necessary to strengthen the optimization of traditional oil drilling monitoring systems. This article summarizes the advantages and disadvantages of the existing image multiscale analysis algorithms, from wavelet transform, stationary wavelet transforms to contourlet transform, and nondownsampled contours based on the characteristics of the images collected by different sensors in the oil drilling monitoring system and the needs of practical applications. Wave transforms detailed comparison of the fusion performance of these image analysis algorithms under the same fusion rules. Aiming at the shortcoming of the large amount of calculation of nonsubsampled contourlet transform, a fast implementation algorithm (IFNSCT) is proposed. The multichannel filter bank structure is used to replace the original tree filter bank structure, which reduces the time-consuming to the original without affecting the analysis performance of the algorithm. One-half of the oil drilling monitoring efficiency has been improved.

Optical and SAR image fusion method with coupling gain injection and guided filtering

“…Our experiments found that the time consumption is mainly concentrated in NSCT MSD and reconstruction. Therefore, we will try some fast MSD methods, such as fast NSCT, 41 fast finite Shearlet transform, 42 etc. In addition, we plan to improve the fusion quality by using some active metric operators that are robust to noise and nonlinear radiometric differences, such as phase congruency features.…”