stochastic seismic inversion
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2021 ◽  
Author(s):  
Anton Khitrenko ◽  
Sergey Fedotkin ◽  
Ayk Nazaryan ◽  
Svetlana Zhigulskiy ◽  
Pavel Emelyanov

Abstract Seismic data is a main source of information for lateral forecast of lithofacies. No one can deny that seismic data is a useful method to determinate structure of prospects. However, we have to accept to urgent need to implement steps that will make possible to predict distribution of lithofacies. In exploration, the prediction of lithology and fluid properties is a main goal. Popularity and comparative simplicity of inversion, made seismic inversion popular for reservoir characterization. Despite the benefits of method, inability to estimate uncertainty of models, stochastic seismic inversion was inveted. A stochastic seismic inversion combine relationship with varying lithofacies parameters and elastic properties using uncertainty of each data. Additional modification of stochastic seismic inversion is geological constraints allows to exclude not appropriate realization and obtain correct probability model of lithofacies. Comparison of approaches and results on a real set provided from the Tyumen formation in Western Siberia allows to estimate advantages and disadvantages of modification stochastic Seismic inversion.


2021 ◽  
Author(s):  
Mehdi Sadeghi ◽  
Navid Amini ◽  
Reza Falahat ◽  
Hamid Sabeti ◽  
Nasser Madani

Author(s):  
Rahmat Catur Wibowo ◽  
Ditha Arlinsky Ar ◽  
Suci Ariska ◽  
Muhammad Budisatya Wiranatanagara ◽  
Pradityo Riyadi

This study has been done to map the distribution of gas saturated sandstone reservoir by using stochastic seismic inversion in the “X” field, Bonaparte basin. Bayesian stochastic inversion seismic method is an inversion method that utilizes the principle of geostatistics so that later it will get a better subsurface picture with high resolution. The stages in conducting this stochastic inversion technique are as follows, (i) sensitivity analysis, (ii) well to seismic tie, (iii) picking horizon, (iv) picking fault, (v) fault modeling, (vi) pillar gridding, ( vii) making time structure maps, (viii) scale up well logs, (ix) trend modeling, (x) variogram analysis, (xi) stochastic seismic inversion (SSI). In the process of well to seismic tie, statistical wavelets are used because they can produce good correlation values. Then, the stochastic seismic inversion results show that the reservoir in the study area is a reservoir with tight sandstone lithology which has a low porosity value and a value of High acoustic impedance ranging from 30,000 to 40,000 ft /s*g/cc.


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