[1]王哲峰,高娜,曾蕊,等.基于深度學習模型的測井電成像空白條帶充填方法[J].測井技術,2019,43(06):578-582.[doi:10.16489/j.issn.1004-1338.2019.06.005]
 WANG Zhefeng,GAO Na,ZENG Rui,et al.A Gaps Filling Method for Electrical Logging Images Based on a Deep Learning Model[J].WELL LOGGING TECHNOLOGY,2019,43(06):578-582.[doi:10.16489/j.issn.1004-1338.2019.06.005]
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基于深度學習模型的測井電成像空白條帶充填方法()
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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:
578-582
欄目:
資料處理
出版日期:
2019-12-15

文章信息/Info

Title:
A Gaps Filling Method for Electrical Logging Images Based on a Deep Learning Model
文章編號:
1004-1338(2019)06-0578-05
作者:
王哲峰高娜曾蕊杜雪菲杜欣睿陳思宇
(中國石油集團測井有限公司長慶分公司,陜西西安710201)
Author(s):
WANG Zhefeng GAO Na ZENG Rui DU Xuefei DU Xinrui CHEN Siyu
(Changqing Branch, China Petroleum Logging CO. LTD., Xi’an, Shaanxi 710201, China)
關鍵詞:
測井評價電成像測井深度學習深度神經網絡空白條帶充填卷積神經網絡大井眼
Keywords:
log evaluation electrical imaging logging deep learning deep neural network gap filling convolution neural network large hole
分類號:
P631.84
DOI:
10.16489/j.issn.1004-1338.2019.06.005
文獻標志碼:
A
摘要:
電成像測井廣泛應用于碳酸鹽巖、砂礫巖和火成巖等復雜儲層的測井評價,對于計算孔隙度、識別裂縫和劃分巖性具有重要作用。但在大井眼的情況下,電成像圖像無法做到全井眼覆蓋,需要對電成像圖像上的空白條帶進行充填,保證后期處理和解釋的精度。結合深度學習框架,提出一種基于卷積神經網絡模型的空白條帶充填方法。在沒有大量學習樣本的情況下,通過優化卷積神經網絡模型結構,捕獲圖像上的大量底層先驗統計特征,實現整幅圖像的結構和紋理特征信息的推理。通過與Filtersim主流充填方法的充填效果比較,發現該方法對于砂泥巖剖面和砂礫巖體的電成像測井圖像空白條帶充填,都具有較好的效果。
Abstract:
Electrical imaging logging has been widely used for evaluating carbonate, glutenite and igneous rock, and plays a very important role in porosity calculation, fracture recognition and lithology classification. However in a large hole, the image cannot cover the whole borehole, and the gaps on the image should be filled to ensure the accuracy of subsequent data processing and interpretation. A gap filling method is proposed based on a convolutional neural network (CNN) after introducing a deep learning framework. It first captures a great deal of low-level image prior statistic information by optimizing the CNN structure without a large amount of training samples, and then infers the structure and texture characteristic on the whole image. Compared with the popular Filtersim method, this method provides much better results for sandy shale and glutenite rock.

參考文獻/References:

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備注/Memo

備注/Memo:
第一作者:王哲鋒,男,1977年生,工程師,從事測井資料解釋及處理研究。E-mail:[email protected](收稿日期: 2019-10-16本文編輯王小寧)
更新日期/Last Update: 2019-12-15
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