知识图谱引导的沉积相智能地震识别技术

doi:10.13810/j.cnki.issn.1000-7210.2024.01.005

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Abstract Relying on the prior knowledge of geological experts， the traditional identification methods for sedimentary facies use seismic and logging data to conduct a qualitative analysis of sedimentary environments with the aid of the storage and computation capacity of computers. As sedimentary facies identification based on seismic data requires a lot of manual interpretation， the accuracy and efficiency are not ideal. How to characterize the geological characteristics of sedimentary microfacies from seismic data and realize the three-dimensional spatial characterization of sedimentary microfacies remains to be studied. In recent years， the knowledge graph has attracted wide attention in the field of geoscience， and the traditional identification method for sedimentary facies can be improved by constructing the knowledge graph as a constraint. However， it is an urgent technical problem to further integrate the knowledge graph， deep learning， and seismic identification technology of sedimentary facies to form a fine identification technology of sedimentary microfacies constrained by the knowledge graph. By introducing geological prior knowledge into the knowledge graph， this paper constructs a high-level semantic cognition system for complex underground sedimentary patterns. The knowledge graph is used for computer representation of geological prior knowledge， which can serve as constraint conditions and quality control measures to guide the identification and modeling of sedimentary microfacies. It ultimately forms an intelligent identification and modeling technology for sedimentary microfacies guided by the knowledge graph. After digitizing geological prior knowledge， the presented method characterizes the spatial distribution of carbonate microbial mound-beach complexes and multi-stage foreset bodies in the Dengying Formation of the central Sichuan Basin. The predicted results are in line with the geological condition of the target area. The proposed method is suitable for deep lithologic trap identification and well demonstration， providing an effective basis for reservoir prediction and has good industrial application value.

Key words： knowledge graph deep learning sedimentary facies seismic facies label intelligent identification

Received: 18 February 2023

PACS:

P631

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	Articles by authors
	YANG Cun
	MENG He
	YE Yueming
	CAO Xiaochu
	YONG Xueshan

Cite this article:

YANG Cun,MENG He,YE Yueming等. Intelligent seismic identification technology of sedimentary facies guided by knowledge graph[J]. OGP, 2024, 59(1): 38-50.

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http://www.ogp-cn.com/EN/10.13810/j.cnki.issn.1000-7210.2024.01.005 OR http://www.ogp-cn.com/EN/Y2024/V59/I1/38

[1]	WU X, LIANG L, SHI Y. et al.FaultSeg3D: using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation[J].Geophysics, 2019, 84(3): IM35-IM45.
[2]	WU X, YAN, QI J, et al. et al. Deep learning for characterizing paleokarst collapse features in 3-d seismic images[J].Journal of Geophysical Research Solid Earth, 2020, 125(9): E2020JB019685.
[3]	WALDELAND A U, JENSEN A C, GELIUS L J, et al. Convolutional neural networks for automated seismic interpretation[J].The Leading Edge, 2018, 37(7): 482-560.
[4]	SHI Y, WU X, FOMEL S. SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural network[J].Interpretation, 2019, 7(3): 1A-T725.
[5]	AO Y, LI H, ZHU L, et al. Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm[J].Journal of Petroleum Science and Engineering, 2019, 173: 781-792.
[6]	SINGH H, SEOL Y, MYSHAKIN E M. Automated well-log processing and lithology classification by identifying optimal features through unsupervised and supervised machine-learning algorithms[J].SPE Journal, 2020, 25(5): 2778-2800.
[7]	GARDNER M,TALUKDAR P,KRISHNAMURTHY J, et al. Incorporating vector space similarity in random walk inference over knowledge bases[C].Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP),2014,397-406.
[8]	陈曦, 陈华钧, 张文. 规则增强的知识图谱表示学习方法[J].情报工程, 2017, 3(1): 26-34.CHEN Xi, CHEN Huajun, ZHANG Wen. Rule-augmented representation learning approach in knowledge graph[J].Technology Intelligence Engineering, 2017, 3(1): 26-34.
[9]	杨存, 孟贺, 叶月明, 等. 沉积相智能地震识别技术研究及应用[J].石油地球物理勘探, 2023, 58(3): 528-539.YANG Cun, MENG He, YE Yueming, et al. Research and application of intelligent seismic identification technology of sedimentary facies[J].Oil Geophysical Prospecting, 2023, 58(3): 528-539.
[10]	李素华, 李蓉, 石国山, 等. 四川盆地井研地区栖霞组白云岩储层识别及有利区预测[J].石油物探, 2023, 62(4): 708-720.LI Suhua, LI Rong, SHI Guoshan, et al. Identification and favorable area prediction of dolomite reservoir of Middle Permian Qixia Formation in Jingyan area, Sichuan Basin[J].Geophysical Prospecting for Petroleum, 2023, 62(4): 708-720.
[11]	周琨. 根据低角度前积地震反射特征划分分流砂坝型浅水三角洲相带[J].石油地球物理勘探, 2023, 58(4): 961-969.ZHOU Kun. Division of distributary sandbar-type shallow-water delta facies belts based on low-angle progradation seismic reflection characteristics[J].Oil Geophysical Prospecting, 2023, 58(4): 961-969.
[12]	刘宝鸿, 郭彦民, 田志, 等. 利用地震相控反演预测辽河坳陷兴隆台潜山中生界致密砂砾岩优质储层[J].石油地球物理勘探, 2022, 57(增刊 2): 100-109.LIU Baohong, GUO Yanmin, TIAN Zhi, et al. Prediction of high-quality tight sandy conglomerate reservoirs in Mesozoic of Xinglongtai buried hill,Liaohe Depression by seismic facies-controlled inversion[J].Oil Geophysical Prospecting, 2022, 57(S2): 100-109.
[13]	QIAN W, FU C, ZHU Y, et al. Translating embeddings for knowledge graph completion with relation attention mechanism[C].Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018, 4286-4292.
[14]	AN B, CHEN B, HAN X P, et al. Accurate text-enhanced knowledge graph representation learning[C].Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018, 745-755.
[15]	ZHU Y, ZHOU W, XU Y, et al. Intelligent learning for knowledge graph towards geological data[J].Scientific Programming, 2017, 2017: 5072427.
[16]	FAN R, WANG L, YAN J, et al. Deep learning-based named entity recognition and knowledge graph construction for geological hazards[J].ISPRS International Journal of Geo-information, 2019, 9(1): 15.
[17]	QIU Q, XIE Z, WU L, et al. Automatic spatiotemporal and semantic information extraction from unstructured geoscience reports using text mining techniques[J].Earth Science Informatics, 2020, 13(4): 1393-1410.
[18]	张富利, 张恩莉, 向永慧, 等. 知识图谱技术在石油天然气勘探开发知识管理中的应用探讨[J].信息系统工程, 2020,(1): 128-131.ZHANG Fuli, ZHANG Enli, XIANG Yonghui, et al. Application of knowledge graph technology in knowledge management of oil and gas exploration and development[J].China CIO News,2020,(1):128-131.