| Issue |
E3S Web of Conf.
Volume 406, 2023
2023 9th International Conference on Energy Materials and Environment Engineering (ICEMEE 2023)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 6 | |
| Section | Geographic Remote Sensing Application and Environmental Modeling | |
| DOI | https://doi.org/10.1051/e3sconf/202340604001 | |
| Published online | 31 July 2023 | |
The Application of Stochastic Seismic Inversion Method of Geostatistical Inversion in Putaohua X Area
Northeast Petroleum University
1 wgwswx@163.com
* 1749676399@qq.com
Stochastic seismic inversion is an important part of geostatistical inversion method, and is the combination of seismic inversion and sequence simulation. In the process of random seismic inversion, firstly, the histogram distribution of random variables is counted, the variation function of random variables is calculated, and the variation range of random variables in different directions is determined. Then, for each implementation, the seismic traces are synthesized at the seismic channel position according to the calculated reflection coefficient, and the error between the synthetic traces and the original seismic traces is compared, and the inversion results are output after the required accuracy is reached. Geostatistical inversion can improve the vertical and horizontal resolution of the reservoir and accurately describe the spatial distribution of the reservoir. Taking the Heidimiao oil layer in the west slope area of Daqing Placanticline as an example, this paper expounds the application of geostatistical inversion in the work area as a whole. Combined with the results of seismic attribute analysis and well logging curve preprocessing, the inversion results formed by accurate time-depth calibration and other processes of reservoir sensitivity parameter analysis provide a basis for optimizing the target well location in the work area.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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