利用土壤地球化学数据和BP神经网络预测松嫩平原油气资源

doi:10.13686/j.cnki.dzyzy.2022.06.010

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (992 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Based on the massive data obtained from the multi-target regional geochemical survey in Northeast China, the back-propagation(BP) neural network is used to establish the model between soil geochemical property and spatial location of oil-gas fields, and construct the optimal prediction model of oil-gas resources. Taking both the 54 soil geochemical indexes and XY coordinate values as input layer of the model and whether the samples are inside the oil-gas fields (1 for inside, 0 for outside) as output layer, the study carries out the model training based on the data of each 500 soil samples randomly selected from inside and outside the oil-gas fields. The results show that the recognition accuracy remains at about 90% after repeated training, indicating that the model has good classification effect and can be used for prediction of oil-gas resources. The hydrocarbon-bearing probability of 11 291 soil samples from Songnen Plain is obtained by using the model, and then the prediction map of oil-gas resources is drawn. The study shows that neural network can play an important role in solving complex nonlinear geological problems.

Key words： neural network prediction model soil geochemistry oil-gas resources big data Songnen Plain

Received: 26 January 2021

PACS:	P596
	P628

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LIU Kai
	ZHU Jian-xin
	DAI Hui-min
	LIU Guo-dong
	XU Jiang
	SONG Yun-hong
	DU Shou-ying

Cite this article:

LIU Kai,ZHU Jian-xin,DAI Hui-min等. PREDICTION OF OIL-GAS RESOURCES IN SONGNEN PLAIN BASED ON SOIL GEOCHEMICAL DATA AND BACK-PROPAGATION NEURAL NETWORK[J]. GEOLOGY AND RESOURCES, 2022, 31(6): 784-789,836.

URL:

http://manu25.magtech.com.cn/Jweb_dzyzy/EN/10.13686/j.cnki.dzyzy.2022.06.010 OR http://manu25.magtech.com.cn/Jweb_dzyzy/EN/Y2022/V31/I6/784

[1]	吴传璧. 中国油气化探50年[J]. 地质通报, 2009, 28(11):1572-1604. Wu C B. Fifty years history of Chinese oil and gas geochemical exploration[J]. Geological Bulletin of China, 2009, 28(11):1572-1604.
[2]	李括, 彭敏, 赵传冬, 等. 全国土地质量地球化学调查二十年[J]. 地学前缘, 2019, 26(6):128-158. Li K, Peng M, Zhao C D, et al. Vicennial implementation of geochemical survey of land quality in China[J]. Earth Science Frontiers, 2019, 26(6):128-158.
[3]	赵君, 汪月华, 张哲寰. 多目标地球化学调查数据在松嫩平原油气藏远景预测的应用[J]. 地质与资源, 2020, 29(6):635-640, 626. Zhao J, Wang Y H, Zhang Z H. Application of multi-target geochemical survey data in prospect prediction of the oil-gas reservoirs in Songnen Plain[J]. Geology and Resources, 2020, 29(6):635-640, 626.
[4]	周亚龙, 孙忠军, 杨志斌, 等. 多目标化探数据与油气藏指标特征的相关性研究[J]. 物探与化探, 2015, 39(3):466-472. Zhou Y L, Sun Z J, Yang Z B, et al. The correlation study of multi-objective geochemical data and index characteristics of the oil and gas reservoir[J]. Geophysical and Geochemical Exploration, 2015, 39(3): 466-472.
[5]	刘艳鹏, 朱立新, 周永章. 卷积神经网络及其在矿床找矿预测中的应用——以安徽省兆吉口铅锌矿床为例[J]. 岩石学报, 2018, 34(11):3217-3224. Liu Y P, Zhu L X, Zhou Y Z. Application of convolutional neural network in prospecting prediction of ore deposits:Taking the Zhaojikou Pb-Zn ore deposit in Anhui Province as a case[J]. Acta Petrologica Sinica, 34(11):2018, 34(11):3217-3224.
[6]	翟明国, 杨树锋, 陈宁华, 等. 大数据时代:地质学的挑战与机遇[J]. 中国科学院院刊, 2018, 33(8):825-831. Zhai M G, Yang S F, Chen N H, et al. Big data epoch:Challenges and opportunities for geology[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(8):825-831.
[7]	陈坤, 张建新. 基于神经网络模型的金矿成矿远景预测——以白马山-龙山地区为例[J]. 地质与资源, 2015, 24(2):160-163. Chen K, Zhang J X. Prediction of gold metallogenic prospect based on the neural network model:A case study of the Baimashan-Longshan area in Hunan Province[J]. Geology and Resources, 2015, 24(2): 160-163.
[8]	赵健, 刘展, 樊彦国, 等. BP神经网络精度估计及其在海洋油气资源预测中的应用[J]. 海洋科学, 2018, 42(11):59-63. Zhao J, Liu Z, Fan Y G, et al. Precision estimation of BP neural network and its application in ocean oil and gas resources prediction [J]. Marine Sciences, 2018, 42(11):59-63.
[9]	陈剑平. 基于MATLAB的神经网络模式识别技术在油气化探中的研究及应用[D]. 北京:中国地质大学, 2008. Chen J P. The research and application of neural network pattern recognition technique for oil and gas[D]. Beijing:China University of Geosciences, 2008.
[10]	郑春雷, 史忠科. 基于神经网络的油气预测方法[J]. 西北工业大学学报, 2003, 21(5):574-577. Zheng C L, Shi Z K. Neural network prediction method and its application to oil and gas forecast[J]. Journal of Northwestern Polytechnical University, 2003, 21(5):574-577.
[11]	王焕弟, 李明, 赵一民. 有监督的人工神经网络油气预测[J]. 铀矿地质, 2001, 17(1):33, 48-55. Li H D, Li M, Zhao Y M. Research on supervised artificial neural network oil and gas prediction[J]. Uranium Geology, 2001, 17(1): 33, 48-55.
[12]	杨丽娜, 解国军. 油气资源丰度预测的人工神经网络方法——以济阳坳陷为例[J]. 石油天然气学报(江汉石油学院学报), 2007, 29(1):4, 55-58. Yang L N, Xie G J. Artificial neural network method for oil and gas resource prediction-a case study of Jiyang depression[J]. Journal of Oil and Gas Technology(Journal of Jianghan Petroleum Institute), 2007, 29(1):4, 55-58.
[13]	奚小环. 大数据与地球系统科学——再论全面发展时期的勘查地球化学[J]. 物探与化探, 2019, 43(3):449-460. Xi X H. Natural resources period:Big data and systematic science of the earth-more on exploration geochemistry during the overall development period[J]. Geophysical and Geochemical Exploration, 2019, 43(3):449-460.
[14]	郭昂青. 松辽盆地油田开发建设对地质环境的负面影响——以大庆油田为例[J]. 地质与资源, 2016, 25(2):176-180. Guo A Q. Negative effect of the development and construction in the oilfield in Songliao Basin on geological environment:A case study of Daqing Oilfield[J]. Geology and Resources, 2016, 25(2):176-180.
[15]	中华人民共和国国土资源部. DZ/T 0258-2014多目标区域地球化学调查规范(1:250000)[S]. 北京:中国标准出版社, 2014:1-42. Ministry of Land and Resources of the People's Republic of China. DZ/T 0258-2014 Specification of multi-purpose regional geochemical survey (1:250000)[S]. Beijing:Standard Press of China, 2014: 1-42.
[16]	Lappmann R P. An introduction to computing with neural nets[J]. IEEE ASSP Magazine, 1987, 4(42):4-22.
[17]	白静, 徐兴友, 陈珊, 等. 松辽盆地长岭凹陷乾安地区青山口组一段沉积相特征与古环境恢复——以吉页油1井为例[J]. 中国地质, 2020, 47(1):220-235. Bai J, Xu X Y, Chen S, et al. Sedimentary characteristics and paleo-environment restoration of the First Member of Qingshankou Formation in Qian'an area, Changling sag, Songliao Basin:A case study of Jiyeyou 1 Well[J]. Geology in China, 2020, 47(1):220-235.
[18]	孙圆辉, 沈平平, 阮宝涛, 等. 松辽盆地长岭断陷长深1号气田火山岩岩性及储渗特征研究[J]. 天然气地球科学, 2008, 19(5): 630-633. Sun Y H, Shen P P, Yuan B T, et al. Lithologic and storage-permeation characteristics of Changshen 1 volcanic gas reservoirs in Jilin[J]. Natural Gas Geoscience, 2008, 19(5):630-633.
[19]	张庆国, 鲍志东, 郭雅君, 等. 扶余油田扶余油层的浅水三角洲沉积特征及模式[J]. 大庆石油学院学报, 2007, 31(3):4-7, 14. Zhang Q G, Bao Z D, Guo Y J, et al. Sedimentary characteristics and model of the Fuyu oil bearing reservoir in Fuyu Oil Field[J]. Journal of Daqing petroleum institute, 2007, 31(3):4-7, 14.
[20]	Li S, Chen J, Xiang J. Applications of deep convolutional neural networks in prospecting prediction based on two-dimensional geological big data[J]. Neural Computing and Applications, 2020, 32(7):2037-2053.
[21]	Chen J P, Xiang J, Hu Q, et al. Quantitative geoscience and geological big data development:a review[J]. Acta Geologica Sinica, 2016, 90(4):1490-1515.