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首頁» 過刊瀏覽» 2023» Vol.8» Issue(1) 1-11???? DOI : 10.3969/j.issn.2096-1693.2023.01.001
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基于地震屬性智能融合的湖相重力流沉積致密砂巖儲層預測
萬曉龍, 劉瑞璟, 時建超, 李偉, 麻書瑋, 李楨, 李士祥, 岳大力, 吳勝和
1 中國石油大學 (北京)油氣資源與探測國家重點實驗室,北京 102249 2 中國石油大學 (北京)地球科學學院,,北京 102249 3 中國石油長慶油田分公司第十一采油廠,,西安 710299 4 中國石油長慶油田分公司勘探開發(fā)研究院,,西安 710018
Prediction of tight sandstone of lacustrine gravity-?ow reservoirs using intelligent fusion of seismic attributes
WAN Xiaolong, LIU Ruijing, SHI Jianchao, LI Wei, MA Shuwei, LI Zhen, LI Shixiang, YUE Dali, WU Shenghe
1 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, China2 College of Geosciences, China University of Petroleum-Beijing, Beijing 102249, China 3 The 11th Oil Production Plant, PetroChina, Changqing Oilfeld Company, Xi’an 710018, China 4 Research Institute of Exploration and Development, PetroChina Changqing Oilfeld Company, Xi’an 710018, China

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摘要? 湖相重力流是目前沉積學研究的熱點與難點,,也是致密油,、頁巖油富集的有利場所,,鄂爾多斯盆地慶城 油田三疊系延長組作為致密油,、頁巖油的典型代表已顯示出巨大的勘探開發(fā)前景。然而,,由于湖相重力流砂體 分布認識不清,,導致油田并未達到預期的開發(fā)效率。本文采用支持向量機(SVR)的機器學習方法,,先優(yōu)選頻段 再優(yōu)選屬性,,建立分頻屬性與測井解釋砂體厚度的非線性映射關系,實現(xiàn)了致密砂巖的定量預測,。研究結(jié)果表 明,,低頻地震屬性適合預測厚層砂體,高頻地震屬性適合預測薄層砂體,;采用機器學習的方法,,將不同頻率的 地震屬性智能融合,能夠兼顧預測不同厚度砂體,,既提高了地震屬性的解釋精度,,又降低了地震解釋的多解性, 實現(xiàn)了砂體厚度的定量預測,。檢驗結(jié)果顯示,,智能融合屬性與砂體厚度的分布趨勢與值域區(qū)間基本一致,智能 融合屬性預測砂體分布的可靠性明顯提高,,與測井解釋砂體厚度的相關性由 0.60 提高至 0.79,,大多數(shù)井點處預 測的砂體厚度誤差小于 5 m。繼而,,根據(jù)融合屬性與測井解釋,,刻畫了研究區(qū)的沉積微相展布特征:研究區(qū)目 的層發(fā)育湖底扇沉積,細分為分支水道,、朵葉主體,、朵葉側(cè)緣、滑塌體與朵葉間 /水道間 5 種沉積微相,;砂體主 體呈扇形連片式沉積,,厚度順物源方向逐漸減薄,;分支水道呈窄條帶狀樹形分叉,,下切發(fā)育于朵葉體之上;朵 葉體沉積為研究區(qū)的沉積主體,;滑塌體為湖底扇前端失穩(wěn)滑塌形成的小規(guī)模孤立砂體,,長軸方向多平行于湖底 扇前端。研究成果對油田下一步高效開發(fā)具有重要意義,。
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關鍵詞 : 地震屬性,;智能融合,;儲層預測,;致密砂巖;湖相重力流
Abstract

The study of lacustrine gravity-flow successions, which are regarded as an important reservoir unit of tight oil and shale oil, has been now a hotspot and also a challenge study work. The Triassic Yanchang Formation in Qingcheng oilfield in Ordos Basin, as a typical reservoir of tight oil and shale oil, shows great exploration and development prospects. However, this oilfield did not achieve the expected development efficiency, probably resulting from the poor understanding of the distribution of lacustrine gravity-flow sandbodies. In this work, proper frequency-decomposed seismic attributes were select relying on their correlation to sand thickness, and then fused using machine learning with a supervised algorithm of support vector machine (SVR). A nonlinear mapping relationship (i.e., the trained SVR model) was established between the frequency-decomposed attributes and the thickness of sandbodies interpreted from well logs, and then the quantitative prediction of tight sandstone was realized through the application of the mapping relationship. The research indicates that: low-frequency seismic attributes are suitable for predicting thick sandbodies, while high-frequency seismic attributes are suitable for predicting thin sandbodies. Utilize advantages of seismic information of different frequencies, and consequently significantly reduces the uncertainty of seismic interpretation, and improves the prediction accuracy of sandbodies, and realizes the quantitative prediction of sandbodies. The test results show that the distribution trend and numerical range of intelligent fusion attrinbute are basically consistent with the sandbodies thickness interpreted by well logs, and the reliability of the sandbodies prediction by intelligent fusion attribute is significantly improved. The correlation between the intelligent fusion attribute and sandbodies thickness interpreted by well logs is improved from 0.6 to 0.79, and the prediction error of sandbodies thickness near the wells is less than 5 m. The geological interpretation indicates that the study strata of target formation are lacustrine-fan deposits, consisting of five sedimentary microfacies: branch channel, main lobe, lateral edge of lobe, slump body and inter lobe / inter channel. The main sandbodies is a fan-shaped, continuous deposition, whose thickness gradually decreases along the provenance direction. The branch channel is branched in the shape of narrow strip trees, which is developed above the lobe. Lobe is the dominated sedimentary microfacies in the study area. The slump body is the small scale isolated sandbodies formed by the collapse in the front of lacustrine-fan deposits. And the long axis direction of slump body is parallel to the front of lacustrine-fan deposits. This research results are of great significance for an efficient development of the oilfield in next stage.

Key words: seismic attribute; intelligent fusion; reservoir characterization; tight sandstone; lacustrine gravity-?ow succession
收稿日期: 2023-02-28 ????
PACS: ? ?
基金資助:中國石油天然氣集團有限公司-中國石油大學( 北京) 戰(zhàn)略合作科技專項項目(ZLZX2020-02),、國家自然科學基金項目(41872107) 聯(lián)合資助
通訊作者: [email protected]
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萬曉龍, 劉瑞璟, 時建超, 李偉, 麻書瑋, 李楨, 李士祥, 岳大力, 吳勝和. 基于地震屬性智能融合的湖相重力流沉積致密砂巖儲層預 測. 石油科學通報, 2023, 01: 1-11 WAN Xiaolong, LIU Ruijing, SHI Jianchao, LI Wei, MA Shuwei, LI Zhen, LI Shixiang, YUE Dali, WU Shenghe. Prediction of tight sandstone of lacustrine gravity-?ow reservoirs using intelligent fusion of seismic attributes. Petroleum Science Bulletin, 2023, 01: 1-11
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