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Inverse-distance weighted modeling method under logging azimuth occlusion |
WANG Zhen-Tao( ) |
Geophysical Research Institute of SINOPEC Shengli Oilfield Branch Company,Dongying 257022,China |
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Abstract Existing log modeling methods fail to consider the directionality of geological deposition, yielding low modeling precision.To overcome this obstacle,this study proposed an inverse-distance weighted (IDW) modeling method under logging azimuth occlusion by analyzing the advantages and disadvantages of the IDW log modeling method.The new method proposed in this study adopted the IDW interpolation formula under azimuth occlusion.In addition to the influencing factors of the distance between known information points and interpolation points,the new method also considered the azimuth occlusion between known information points.Moreover,this study quantitatively described the influence of azimuth occlusion between known samples by integrating the weighting coefficients of non-azimuth occlusion.As indicated by the comparison between the new method and conventional methods based on numerical experiments and actual data,the modeling results of the new method showed gentler and more natural spatial variation,which conformed to the variation patterns of geological deposition.In sum,the method proposed in this study reflects the spatial continuity and directionality of geological deposition,has distinct advantages in improving the precision of geological log modeling,and thus can be widely applied to practical applications.
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Received: 21 March 2022
Published: 05 July 2023
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Schematic diagram of logging azimuth occlusion inverse distance interpolation
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Actual velocity model flat slice
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Velocity modeling slice with 60 simulated logging points a—inverse distance weighted modeling of logging;b—inverse distance weighting modeling under logging azimuth occlusion
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Velocity modeling slice with 120 simulated logging points a—inverse distance weighted modeling of logging;b—inverse distance weighting modeling under logging azimuth occlusion
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Velocity modeling profile of SL oil field a—inverse distance weighted modeling of logging;b—inverse distance weighting modeling under logging
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Velocity modeling slice of SL oil field a—inverse distance weighted modeling of logging;b—inverse distance weighting modeling under logging
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