Application of cloud transforms in seismic reservoir prediction

Leisong, Liu; Jun, Gao; Shuanghe, Dai; Yang, Yu

doi:10.1190/segam2013-0534.1

Cited by 6 publications

(1 citation statement)

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“…In this study, seismic wave impedance Bayes inversion genetic algorithm and cloud transform [13] gamma attribute prediction method were adopted to predict the sandstone reservoirs in coal-bearing strata in Block K. The results demonstrate that the sandstone development area and spatial distribution law are predicted accurately, thus providing reliable technical support for the increment of reserves and production in this block.…”

Section: Introductionmentioning

confidence: 97%

Application of Reservoir-Predicting Technique to Thin Sandstones in Coal-Bearing Strata in Block K

Leisong¹

2018

OGCE

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In Block K of South Turgay Basin in central Kazakhstan, the development of target, Aibalin, is controlled by the boundary of graben (especially rift-type stratigraphy-lithology assemblage). The Aibalin Fm is mainly developed with delta and lakeshore swamp facies, and composed of grey sandstone, siltstone, shale and coal-bearing strata, with extensive carbonized vegetal debris. Moreover, it contains thin and horizontally-variable reservoirs. Coal beds affect seismic survey greatly. Because of the influence of tuning effect in seismic data, thin sandstone reservoir distribution and physical properties cannot be reflected accurately in seismic data. Meanwhile, thin sandstone reservoir cannot be effectively predicted through seismic-based conventional inversion methods and processes. In this paper, a new prediction process for thin sandstone reservoir in this block is proposed, contributing to the effective prediction of thin sandstone reservoir distribution and physical properties. Firstly, sensitive parameters for lithology interpretation are defined and lithology interpretation template was established, through comprehensive analysis of drilling, logging and seismic data. Secondly, seismic wave impedance Bayes inversion genetic algorithm and cloud transform gamma attribute prediction technique are used to derive wave impedance and gamma data volume. Finally, the wave impedance and gamma data volume are combined with lithology interpretation template to predict the physical properties of the reservoirs.

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Section: Introductionmentioning

confidence: 97%