[1]毛永強,李寧,田軍,等.利用測井曲線圖論多分辨率聚類識別松南地區火山巖巖性[J].測井技術,2019,43(06):642-646.[doi:10.16489/j.issn.1004-1338.2019.06.017]
 MAO Yongqiang,LI Ning,TIAN Jun,et al.Multi-resolution Clustering Identification of Volcanic Rocks Using Logging Curve Graph Theory in Songnan Area[J].WELL LOGGING TECHNOLOGY,2019,43(06):642-646.[doi:10.16489/j.issn.1004-1338.2019.06.017]
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利用測井曲線圖論多分辨率聚類識別松南地區火山巖巖性()
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《測井技術》[ISSN:1004-1338/CN:61-1223/TE]

卷:
第43卷
期數:
2019年06期
頁碼:
642-646
欄目:
解釋評價
出版日期:
2019-12-15

文章信息/Info

Title:
Multi-resolution Clustering Identification of Volcanic Rocks Using Logging Curve Graph Theory in Songnan Area
文章編號:
1004-1338(2019)06-0642-05
作者:
毛永強李寧田軍曹開芳
(中國石化東北油氣分公司勘探開發研究院,吉林長春130062)
Author(s):
MAO Yongqiang LI Ning TIAN Jun CAO Kaifang
(Oil and Gas Exploration and Development Research Institute of Northeast Oil and Gas Company, SINOPEC, Changchun, Jilin 130062, China)
關鍵詞:
測井解釋松遼盆地中基性火山巖圖論多分辨率聚類巖性識別測井響應特征
Keywords:
log interpretation Songliao Basin intermediate and basic igneous rock multi-resolution clustering of graph theory lithology identification logging response characteristics
分類號:
P631.84
DOI:
10.16489/j.issn.1004-1338.2019.06.017
文獻標志碼:
A
摘要:
由于鉆井提速和火山巖巖性本身的復雜性,常規巖屑識別火山巖巖性存在多解性。在松南地區,中基性火山巖與碎屑巖不管在測井曲線值域還是在測井識別圖版上疊置都較為嚴重,而火山巖取心層段有限、特殊測井費用高昂,因此能否經濟有效地識別火山巖巖性制約著火山巖期次劃分及儲層深入的分析和研究。在深入挖掘中基性火山巖與碎屑巖、中基性火山巖不同巖性類別之間在常規測井響應特征差異的基礎上,采用了圖論多分辨率聚類的方法,深度學習每種巖性在多條測井曲線上的正態分布范圍,通過神經網絡算法將具有相似分布特征的層段進行聚類,最后根據測井曲線范圍分布確定巖性類別。該方法對中基性火山巖巖性劃分起到了良好效果,與實際地質認識及取心認識相符,在其他巖性識別上也具有推廣意義。
Abstract:
Volcanic lithology identification is the basis of reservoir research. Due to increasing ROP and the complexity of volcanic lithology, multiple solutions may appear when using conventional debris to identify volcanic lithology. Especially in the Songnan area, intermediate and basic volcanic rocks and clastic rocks are overlaid seriously on logging curves or logging identification charts. In addition, the volcanic section that can be cored is limited and unconventional logging costs are high, therefore, whether the identification of volcanic rocks is economic and effective restricts the classification of volcanic rocks and the deep analysis and research of volcanic reservoirs. This paper firstly studies the difference of conventional logging response characteristics between intermediate and basic volcanic rocks and clastic rocks, and among different lithologic categories of intermediate and basic volcanic rocks, then deeply learns the normal distribution of each lithology on multiple logging curves using multi-resolution clustering of graph theory, and clusters the sections with similar distribution characteristics using a neural network algorithm, and finally determines lithology categories according to the distribution of logging curves. This method is effective for dividing the lithology of intermediate and basic volcanic rocks. The result is consistent with actual geological and core data. It is also applicable for identifying other types of lithology.

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備注/Memo

備注/Memo:
第一作者:毛永強,男,1971年生,高級工程師,碩士,研究方向勘探開發綜合評價。E-mail:[email protected](收稿日期: 2019-01-25本文編輯王小寧)
更新日期/Last Update: 2019-12-15
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