Identification of offshore oil and gas platform in the Bohai Sea based on multi-source satellite remote sensing

doi:10.3969j.issn.1001-909X.2022.04.008

Abstract

Abstract: To solve the problem of insufficient information of offshore oil and gas platform, the method of oil and gas platform identification based on multi-source satellite remote sensing was studied. Based on Landsat-8 remote sensing images of the Bohai Sea (2018-2021), 136, 166 and 113 oil and gas platforms in the Bohai Sea were identified by threshold segmentation, K-means unsupervised algorithm and maximum likelihood classification, respectively. Based on Sentinel-1 SAR images (2018-2021), 338 oil and gas platforms were identified by threshold segmentation method. Based on the decision level fusion of the above results, 428 oil and gas platforms in the Bohai Sea were identified. The ZY-3 high-resolution images were used to verify the identification results of the fusion method. The results showed that the accuracy of the identified oil and gas platforms reached 85.2%, and the error rate and miss rate were 10.9% and 3.9%, respectively. The identified oil and gas platform locations are consistent with literature and public data. The research shows that the decision level fusion method can realize the effective identification and extraction of offshore oil and gas platforms, and has the value of popularization and application.

Key words: satellite remote sensing, offshore oil and gas platform, multi-source remote sensing, identification, the Bohai Sea

CLC Number:

TP75

LU Aiying, LI Peng, ZHU Haitian, CHEN Peng, ZHAO Yizhi. Identification of offshore oil and gas platform in the Bohai Sea based on multi-source satellite remote sensing[J]. Journal of Marine Sciences, 2022, 40(4): 82-89.

References

[1] 王义,李丽.南海油气钻井平台遥感监测研究[J].中国地质调查,2021,8(3):58-63.
WANG Yi, LI Li, Remote sensing monitoring for the oil and gas platform in the South China Sea[J]. Geological Survey of China, 2021, 8(3): 58-63.
[2] 门晓勇.SAR图像舰船目标检测技术研究[D].哈尔滨:哈尔滨工程大学,2020.
MEN Xiaoyong. Research on ship target detection technology in SAR image[D]. Harbin: Harbin Engineering University,
2020.
[3] 王加胜,刘永学,李满春,等.基于ENVISAT ASAR的海洋钻井平台遥感检测方法:以越南东南海域为例[J].地理研究,2013,32(11):2143-2152.
WANG Jiasheng, LIU Yongxue, LI Manchun, et al. Drilling platform detection based on ENVISAT ASAR remote sensing data: A case of southeastern Vietnam offshore area[J]. Geographical Research, 2013, 32(11): 2143-2152.
[4] 万剑华,姚盼盼,孟俊敏,等.基于SAR影像的海上石油平台识别方法研究[J].测绘通报,2014(1):56-59.
WAN Jianhua, YAO Panpan, MENG Junmin, et al. Research on detection method of the offshore oil platform based on SAR images[J]. Bulletin of Surveying and Mapping, 2014(1): 56-59.
[5] 贾坤,李强子,田亦陈,等.遥感影像分类方法研究进展[J].光谱学与光谱分析,2011,31(10):2618-2623.
JIA Kun, LI Qiangzi, TIAN Yichen, et al. A review of clas-sification methods of remote sensing imagery[J]. Spectros-copy and Spectral Analysis, 2011, 31(10): 2618-2623.
[6] ELDHUSET K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(4): 1010-1019.
[7] LIU Yongxue, SUN Chao, YANG Yuhao, et al. Automatic extraction of offshore platforms using time-series Landsat-8 Operational Land Imager data[J]. Remote Sensing of Environment, 2016, 175: 73-91.
[8] 赵赛帅,孙超,王海江,等.基于Landsat遥感影像的海上油气平台提取与监测[J].热带地理,2017,37(1):112-119.
ZHAO Saishuai, SUN Chao, WANG Haijiang, et al. Extraction and monitoring of offshore oil and gas platforms based on Landsat imagery[J]. Tropical Geography, 2017, 37(1): 112-119.
[9] 李强,苏奋振,王雯玥.基于VIIRS数据的油气平台提取技术研究[J].地球信息科学学报,2017,19(3):398-406.
LI Qiang, SU Fenzhen, WANG Wenyue. Research on oil and gas platform extraction technology based on VIIRS data[J]. Journal of Geo-Information Science, 2017, 19(3): 398-406.
[10] LIU Yongxue, SUN Chao, SUN Jiaqi, et al. Satellite data lift the veil on offshore platforms in the South China Sea[J]. Scientific Reports, 2016, 6: 33623.
[11] 孟若琳,邢前国.基于可见光的海上船舶油井平台遥感检测[J].计算机应用,2013,33(3):708-711.
MENG Ruolin, XING Qianguo. Detection of offshore ship and well platform based on optical remote sensing images[J]. Journal of Computer Applications, 2013, 33(3): 708-711.
[12] 孙超.长时间序列多源遥感影像支持下南海油气开发活动监测研究[D].南京:南京大学,2018.
SUN Chao, Dynamic monitoring of oil/gas development in the South China Sea based on long-period time-series and multi-source remote sensing images[D]. Nanjing: Nanjing University, 2018.
[13] 薛永安,王德英,王飞龙,等.渤海海域凝析油气藏、轻质油藏形成条件与勘探潜力[J].石油学报,2021,42(12):1581-1591.
XUE Yongan, WANG Deying, WANG Feilong,et al. Formation conditions and exploration potential of condensate and light oil reservoirs in Bohai Sea[J]. Acta Petrolei Sinica, 2021, 42(12): 1581-1591.
[14] 江尚昆,王德英,孙哲,等.渤海油田油气勘探阶段及储量增长潜力[J].海洋地质前沿,2022,38(2):48-54.
JIANG Shangkun, WANG Deying, SUN Zhe, et al. Oil and gas exploration stage and potential of reserve growth of the Bohai Oilfield[J]. Marine Geological Frontiers, 2022, 38(2): 48-54.
[15] MACQUEEN J. Some methods for classification and analysis of multivariate observation[C]//Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probabilit. Berkeley: University of California Press, 1967: 281-297.
[16] STRAHLER A H. The use of prior probabilities in maximum likelihood classification of remotely sensed data[J]. Remote Sensing of Environment, 1980, 10: 135-163.
[17] WU Guofeng, LEEUW J D, SKIDMORE A K, et al. Performance of Landsat TM in ship detection in turbid waters[J]. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(1): 54-61.
[18] 贾诗超,薛东剑,李成绕,等.基于Sentinel-1 SAR数据的水体信息提取方法研究[J].人民长江,2019,50(2):213-217.
JIA Shichao, XUE Dongjian, LI Chengrao, et al. Study on new method for water area information extraction based on Sentinel-1 SAR data[J]. Yangtze River, 2019, 50(2): 213-217.
[19] 李强. 基于多源数据的南海油气平台检测及油气资源安全态势评价[D].兰州:兰州交通大学,2016.
LI Qiang. Oil and gas platforms detection and oil and gas resources security situation evaluate based on multi-source data in the South China Sea[D]. Lanzhou: Lanzhou Jiaotong University, 2016.
[20] 陈华,陈书海,张平,等.K-means算法在遥感分类中的应用[J].红外与激光工程,2000,29(2):26-30.
CHEN Hua, CHEN Shuhai, ZHANG Ping, et al. Application of K-means classification in remote sensing[J]. Infrared and Laser Engineering, 2000, 29(2): 26-30.
[21] 樊利恒,吕俊伟,于振涛,等.基于改进最大似然方法的多光谱遥感图像分类方法[J].电光与控制,2014,21(10):52-56,74.
FAN Liheng, LÜ Junwei, YU Zhentao, et al. A multi-spectral remote sensing image classification technique based on improved ML algorithom[J]. Electronics Optics & Control, 2014, 21(10): 52-56, 74.
[22] LIU Xin, MENG Ruolin, XING Qianguo, et al. Assessing oil spill risk in the Chinese Bohai Sea: A case study for both ship and platform related oil spills[J]. Ocean & Coastal Management, 2015, 108: 140-146.
[23] 邴磊.基于遥感和GIS的海上溢油风险识别及区划研究[D].烟台:中国科学院大学(中国科学院烟台海岸带研究所),2019.
BING Lei. Oil spill risk identification and zonation at sea based on remote sensing and GIS[D]. Yantai: Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, 2019.
[24] 薛永安,韦阿娟,彭靖淞,等.渤海湾盆地渤海海域大中型油田成藏模式和规律[J].中国海上油气,2016,28(3):10-19.
XUE Yongan, WEI Ajuan, PENG Jingsong, et al. Accumu-lation models and regularities of large-middle scale oilfields in Bohai Sea, Bohai Bay Basin[J]. China Offshore Oil and Gas, 2016, 28(3): 10-19.
[25] 周守为,李清平.开发海洋能源,建设海洋强国[J].科技导报,2020,38(14):17-26.
ZHOU Shouwei, LI Qingping. Developing marine energy and building a marine power[J]. Science & Technology Review, 2020, 38(14): 17-26.