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您的检索式：作者名="YIN Yanshu"
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| 1 | Influence Factors on the Distribution of Tidal Bar in Tide-Dominated Estuary: Insight from Deposition Numerical Simulation显示文摘Objective The Mcmurray Formation oil sands in the Athabasca region of Canada are mainly a deposit of tide-dominated estuary and an important target of oil and gas exploration of CNPC.However,the outcrop and the subsurface reservoir of tide-dominated estuary are few,and the formation mechanisms and distribution of estuarine sand bar are ambiguous due to the interaction of fluvial and | YIN Yanshu ZHOU Han FENG Wenjie HUANG Jixin LIU Shangqi | 2018 | Acta Geologica Sinica(English Edition)2018，92，5： | 1 |
| 2 | A training image optimization method in multiple-point geostatistics and its application in geological modeling显示文摘Based on the analysis of the high-order compatibility optimization method proposed by predecessors, a new training image optimization method based on data event repetition probability is proposed. The basic idea is to extract the data event contained in the condition data and calculate the number of repetitions of the extracted data events and their repetition probability in the training image to obtain two statistical indicators, unmatched ratio and repeated probability variance of data events. The two statistical indicators are used to characterize the diversity and stability of the sedimentary model in the training image and evaluate the matching of the geological volume spatial structure contained in data of the well block to be modeled. The unmatched ratio reflects the completeness of geological model in training image, which is the first choice index. The repeated probability variance reflects the stationarity index of geological model of each training image, and is an auxiliary index. Then, we can integrate the above two indexes to achieve the optimization of training image. Multiple sets of theoretical model tests show that the training image with small variance and low no-matching ratio is the optimal training image. The method is used to optimize the training image of turbidite channel in Plutonio oilfield in Angola. The geological model established by this method is in good agreement with the seismic attributes and can better reproduce the morphological characteristics of the channels and distribution pattern of sands. | WANG Lixin YIN Yanshu FENG Wenjie DUAN Taizhong ZHAO Lei ZHANG Wenbiao | 2019 | Petroleum Exploration and Development2019，46，4： | 0 |
| 3 | A method of reconstructing 3D model from 2D geological cross-section based on self-adaptive spatial sampling:A case study of Cretaceous McMurray reservoirs in a block of Canada显示文摘An orthogonal 2D training image is constructed from the geological analysis results of well logs and sedimentary facies;the 2 D probabilities in three directions are obtained through linear pooling method and then aggregated by the logarithmic linear pooling to determine the 3 D multi-point pattern probabilities at the unknown points,to realize the reconstruction of a 3 D model from 2D cross-section.To solve the problems of reducing pattern variability in the 2 D training image and increasing sampling uncertainty,an adaptive spatial sampling method is introduced,and an iterative simulation strategy is adopted,in which sample points from the region with higher reliability of the previous simulation results are extracted to be additional condition points in the following simulation to improve the pattern probability sampling stability.The comparison of lateral accretion layer conceptual models shows that the reconstructing algorithm using self-adaptive spatial sampling can improve the accuracy of pattern sampling and rationality of spatial structure characteristics,and accurately reflect the morphology and distribution pattern of the lateral accretion layer.Application of the method in reconstructing the meandering river reservoir of the Cretaceous McMurray Formation in Canada shows that the new method can accurately reproduce the shape,spatial distribution pattern and development features of complex lateral accretion layers in the meandering river reservoir under tide effect.The test by sparse wells shows that the simulation accuracy is above 85%,and the coincidence rate of interpretation and prediction results of newly drilled horizontal wells is up to 80%. | WANG Lixin YIN Yanshu WANG Hui ZHANG Changmin FENG Wenjie LIU Zhenkun WANG Pangen CHENG Lifang LIU Jiong | 2021 | Petroleum Exploration and Development2021，48，2： | 0 |
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