Neural-Computer Modelling for Reservoir Properties Prediction from Stochastic Seismic Inversion Results

2006 ◽  
Author(s):  
Gritsenko Alexey
Keyword(s):  
Computer Modelling ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Stochastic Seismic Inversion

Multivariate stochastic seismic inversion with adaptive sampling

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. R429-R448 ◽  
Author(s):  
Maryam Hadavand Siri ◽  
Clayton V. Deutsch
Keyword(s):  
Seismic Data ◽  
Adaptive Sampling ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Stochastic Inversion ◽  
Geostatistical Techniques ◽  
Multiple Reservoir ◽  
Stochastic Seismic Inversion ◽  
Close Integration

We have developed a fully coupled categorical-multivariate continuous stochastic inversion with a combined petro-elastic model and convolution. The new multivariate stochastic seismic inversion approach simulates multiple reservoir properties simultaneously and conditions them to the well and seismic data at the same time through the close integration of multivariate geostatistical modeling and stochastic inversion. This approach combines a trace-by-trace (column-wise) adaptive sampling algorithm with multivariate geostatistical techniques to select reservoir properties that match the seismic data. The adaptive sampling method uses an acceptance-rejection approach to condition geostatistical models to the well and seismic data. The adaptive sampling algorithm defines a practical stopping criteria based on the inherent uncertainty due to modeling assumptions and the size of the uncertainty space. This technique samples the realizations inside the space of uncertainty; the number of realizations attempted increases with the size of the space of uncertainty. Characterizing multiple reservoir properties simultaneously through the close integration of seismic inversion and multivariate geostatistical techniques leads to improved high-resolution reservoir property models that reproduce the original seismic data. A case study is considered to compare the proposed stochastic inversion approach with the conventional methods. The case study represents multivariate stochastic inversion provides high-resolution facies and reservoir physical properties simultaneously that reproduce the original seismic data within quality of data better than the other approaches.

Global stochastic seismic inversion using turning bands simulation and co-simulation

2021 ◽  
Author(s):  
Mehdi Sadeghi ◽  
Navid Amini ◽  
Reza Falahat ◽  
Hamid Sabeti ◽  
Nasser Madani
Keyword(s):  
Seismic Inversion ◽  
Turning Bands ◽  
Stochastic Seismic Inversion

scholarly journals Gas Saturated Sandstone Reservoir Modeling Using Bayesian Stochastic Seismic Inversion

2020 ◽  
Vol 5 (1) ◽  
pp. 25-31
Author(s):  
Rahmat Catur Wibowo ◽  
Ditha Arlinsky Ar ◽  
Suci Ariska ◽  
Muhammad Budisatya Wiranatanagara ◽  
Pradityo Riyadi
Keyword(s):  
Scale Up ◽  
Seismic Inversion ◽  
Well Logs ◽  
Reservoir Modeling ◽  
Inversion Method ◽  
Tight Sandstone ◽  
Stochastic Inversion ◽  
A Value ◽  
Sandstone Reservoir ◽  
Stochastic Seismic Inversion

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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