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-flow succession

Received： 2022-08-19

Corresponding Authors：yuedali@cup.edu.cn

Cite this article：万晓龙, 刘瑞璟, 时建超, 李伟, 麻书玮, 李桢, 李士祥, 岳大力, 吴胜和. 基于地震属性智能融合的湖相重力流沉积致密砂岩储层预 测. 石油科学通报, 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-flow reservoirs using intelligent fusion of seismic attributes. Petroleum Science Bulletin, 2023, 01: 1-11

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