shaohua li

Dr. shaohua li

orcid

Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 April 2021 in Journal of Zhejiang University-SCIENCE A
Verified
Journal Article
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SLshaohua li
shaohua li
MZMing-Ju Zhang
Ming-Ju Zhang
Pengfei LiPengfei Li
Pengfei Li
This paper focuses on the ground settlement induced by the construction of a curved shield tunnel. Ground loss and construction loadings are the two factor

29 October 2021 in Journal of Petroleum Science and Engineering
Verified
Journal Article
Closed access
XWXixin Wang
Xixin Wang
YSYU SIYU
YU SIYU
SLshaohua li
shaohua li
NZNaidan Zhang
Naidan Zhang

16 July 2021 in Remote Sensing
Verified
Journal Article
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RSRan ShuHao
Ran ShuHao
XGXIANJUN GAO
XIANJUN GAO
YYYuanwei Yang
Yuanwei Yang
SLshaohua li
shaohua li
GZGuangbin Zhang
Guangbin Zhang
PWPing Wang
Ping Wang
Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.

29 January 2022 in Lithosphere
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Journal Article
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XWXixin Wang
Xixin Wang
XZXinrui Zhou
Xinrui Zhou
SLshaohua li
shaohua li
NZNaidan Zhang
Naidan Zhang
LJLing Ji
Ling Ji
HLHang Lu
Hang Lu
Mechanisms that lead to different quantities of hydrocarbon accumulation in complex fault blocks are a major subject that impacts further development plans of oil and gas fields. To better understand such mechanisms, fault activity has been interpreted along with existing electron micrographs from the fault zone, petrophysical data, and the occurrence of the seismic pump. This enabled us to investigate the controlling mechanisms of the growth faults and other associated faults with the main growth fault in the Wang Guantun area that could have impacted hydrocarbon distribution. The results showed that the activity of Kongdong growth fault is periodic and intermittent, which produced strong seismic pumping action. Furthermore, a series of secondary faults were generated in adjacent strata due to the fault activity, which could have led to the formation of a secondary seismic pump source. A combination of these two incidents is believed to influence the differential distribution of hydrocarbons in the area, in fault-associated reservoirs. Ultimately, we correlated the activity of the growth fault to the strength of the pumping force causing the distribution of hydrocarbons in the active parts of the faults (pump source position) on the horizontal plane and vertically located reservoirs to be more dominant.

29 April 2021 in Geofluids
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XWXixin Wang
Xixin Wang
FZFan Zhang
Fan Zhang
SLshaohua li
shaohua li
LDLUXING DOU
LUXING DOU
YLyuming liu
yuming liu
WZWen Zhao
Wen Zhao
The architecture analysis of the different orders sedimentary bodies is of great significance to the efficient development of oil and gas fields. In order to investigate the effects of the architectural interfaces on reservoir quality and heterogeneity, this study takes the Gudong oil field as a case to investigate the architectural characteristics of hierarchical bounding surfaces using detailed descriptions of core and wireline logs. Architectural models from the 7th-order to the 3rd-order are analyzed, and the developmental characteristics of the 5th-order braided river, 4th-order single sandstone, and 3rd-order accretion are summarized. The interlayer between two braided rivers is floodplain mud deposition, with poor physical properties, stable thickness, and strong blocking capacity. Two models of interlayers are found in the 4th-order deposition. The first interlayer is between the braided filling channel and midchannel bar, which is composed of generally fine-grained sediments with calcium cementation and poor physical properties. The second interlayer is a transformation belt between two midchannel bars and is generally composed of gravel-scoured deposition with penetration capability. The 3rd-order surfaces are defined as the surfaces of accretions within midchannel bars. Two models of interlayers are also found in the 3rd-order surfaces of accretions. The paleocurrent of the sandy braided river is reconstructed by synthesizing the core data, well logging data, and production performance data. A total of 1 fluvial system (7th-order), 2 compound braided rivers (6th-order), 11 braided rivers (5th-order), 41 midchannel bars (4th-order), and 96 accretions (3rd-order) are developed in the study area.

5 September 2019 in Journal of Thermal Analysis and Calorimetry
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Journal Article
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LGLong Gao
Long Gao
XSXuegeng Sun
Xuegeng Sun
BSBaizhong Sun
Baizhong Sun
DCDeyong Che
Deyong Che
SLshaohua li
shaohua li
ZLZhongze Liu
Zhongze Liu