[1]陳鋼花,梁莎莎,王軍,等.卷積神經網絡在巖性識別中的應用[J].測井技術,2019,43(02):129-134.[doi:10.16489/j.issn.1004-1338.2019.02.004]
 CHEN Ganghua,LIANG Shasha,WANG Jun,et al.Application of Convolutional Neural Network in Lithology Identification[J].WELL LOGGING TECHNOLOGY,2019,43(02):129-134.[doi:10.16489/j.issn.1004-1338.2019.02.004]
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卷積神經網絡在巖性識別中的應用()
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《測井技術》[ISSN:1004-1338/CN:61-1223/TE]

卷:
第43卷
期數:
2019年02期
頁碼:
129-134
欄目:
處理解釋
出版日期:
2019-06-20

文章信息/Info

Title:
Application of Convolutional Neural Network in Lithology Identification
文章編號:
1004-1338(2019)02-0129-06
作者:
陳鋼花1 梁莎莎1 王軍2 隋淑玲2
(1.中國石油大學(華東)地球科學與技術學院, 山東 青島 266580; 2.中國石油化工股份有限公司勝利油田分公司勘探開發研究院, 山東 東營 257000)
Author(s):
CHEN Ganghua1 LIANG Shasha1 WANG Jun2 SUI Shuling2
(1. School of Geosciences, China University of Petroleum, Qingdao, Shandong 266580, China; 2. Research Institute of Exploration and Development, Shengli Oilfield Company, SINOPEC, Dongying, Shandong 257000, China)
關鍵詞:
測井解釋 深度學習 卷積神經網絡 巖性識別
Keywords:
logg interpretation deep learning convolutional neural network lithology identification
分類號:
P631.84
DOI:
10.16489/j.issn.1004-1338.2019.02.004
文獻標志碼:
A
摘要:
深度學習是人工智能中的一個重要部分,卷積神經網絡作為深度學習一個分支,用多層非線性計算單元可以表達高度非線性和高變度函數。提出將卷積神經網絡應用于判別儲層巖性的方法,構建了一個雙層的卷積神經網絡模型,樣本回判準確率為99%。通過把卷積神經網絡方法與巖石物理相方法和支持向量機方法進行對比,分析卷積神經網絡方法準確率高、速度快,巖性預測具有實時性。由此證明卷積神經網絡在儲層巖性識別中的適用性,且準確率較高。
Abstract:
Deep learning is an important part of artificial intelligence. As a branch of deep learning, convolutional neural networks can express highly nonlinear and highly variable functions with multi-layer nonlinear computing units. A method of using convolution neural network to discriminate reservoir lithology is proposed, and a two-layer convolution neural network model is established in this paper. The accuracy of sample retroaction is 99%. Compared with petrophysical facies and support vector machine, the convolution neural network method predicts reservoir lithology accurately, fast and real-time. It has been proved applicable for reservoir lithology identification.

參考文獻/References:

[1] RUMELHART D E. Paralled distributed processing, 1&2 [M]. MIT Press: Cambridge, MA, 1986. [2] 劉爭平, 何永富. 人工神經網絡在測井解釋中的應用 [J]. 地球物理學報, 1995(增刊1): 323-330. [3] 楊輝, 黃健全, 胡雪濤, 等. BP神經網絡在致密砂巖氣藏巖性識別中的應用 [J]. 油氣地球物理, 2013, 11(1): 39-42. [4] HUBEL D, WIESEL T. Receptive fields, binocular interaction, and function architecture in the cat's visal cortex [J]. Journal of Physiology, 1962, 160: 106-154. [5] LECUN Y, BOSER B. Back propagation applied to handwritten zip code recognition [J]. Neural Computation, 1989, 1(4): 541-551. [6] LECUN Y, BOTTOU L, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324. [7] HITON G E, SALAKHUTDIOV R R. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507. [8] 段友祥, 李根田, 孫歧峰. 卷積神經網絡在儲層預測中的應用研究 [J]. 通信學報, 2016, 37(增刊1): 1-9. [9] HITON G E, OSINDERO S, THE Y W. A fast learning algorithm for deep belief nets [J]. Neural Computation, 2006, 18(7): 1527-1554. [10] 董寧芳. 淺析沾化凹陷構造演化對沙四段成藏的控制作用 [J]. 中國新技術新產品, 2013(7): 135-136.

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備注/Memo

備注/Memo:
(修改回稿日期: 2018-09-17 本文編輯 余迎)基金項目: 國家科技重大專項“渤海灣盆地濟陽坳陷致密油開發示范工程”(2017ZX05072002) 第一作者: 陳鋼花,女,教授,從事測井資料數字處理與綜合解釋、測井資料在地質、油藏及鉆井工程中的應用、非均質油氣藏測井評價等教學科研工作。E-mail:[email protected]
更新日期/Last Update: 2019-06-20
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