Comparing the Odisi Integrated Stochastic Seismic Inversion Method to an Equivalent Bayesian Inversion Method

2017 ◽  
Author(s):  
M. Walker ◽  
S. Grant ◽  
H. Pham Huu ◽  
Y. Zheng
Keyword(s):  
Seismic Inversion ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
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.

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 Estimates of European emissions of methyl chloroform using a Bayesian inversion method

2014 ◽  
Vol 14 (18) ◽  
pp. 9755-9770 ◽  
Author(s):  
M. Maione ◽  
F. Graziosi ◽  
J. Arduini ◽  
F. Furlani ◽  
U. Giostra ◽  
...  
Keyword(s):  
Point Source ◽  
High Frequency ◽  
Stratospheric Ozone ◽  
Montreal Protocol ◽  
Source Analysis ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Data Set ◽  
Methyl Chloroform

Abstract. Methyl chloroform (MCF) is a man-made chlorinated solvent contributing to the destruction of stratospheric ozone and is controlled under the "Montreal Protocol on Substances that Deplete the Ozone Layer" and its amendments, which called for its phase-out in 1996 in developed countries and 2015 in developing countries. Long-term, high-frequency observations of MCF carried out at three European sites show a constant decline in the background mixing ratios of MCF. However, we observe persistent non-negligible mixing ratio enhancements of MCF in pollution episodes, suggesting unexpectedly high ongoing emissions in Europe. In order to identify the source regions and to give an estimate of the magnitude of such emissions, we have used a Bayesian inversion method and a point source analysis, based on high-frequency long-term observations at the three European sites. The inversion identified southeastern France (SEF) as a region with enhanced MCF emissions. This estimate was confirmed by the point source analysis. We performed this analysis using an 11-year data set, from January 2002 to December 2012. Overall, emissions estimated for the European study domain decreased nearly exponentially from 1.1 Gg yr−1 in 2002 to 0.32 Gg yr−1 in 2012, of which the estimated emissions from the SEF region accounted for 0.49 Gg yr−1 in 2002 and 0.20 Gg yr−1 in 2012. The European estimates are a significant fraction of the total semi-hemisphere (30–90° N) emissions, contributing a minimum of 9.8% in 2004 and a maximum of 33.7% in 2011, of which on average 50% are from the SEF region. On the global scale, the SEF region is thus responsible for a minimum of 2.6% (in 2003) and a maximum of 10.3% (in 2009) of the global MCF emissions.

BAYESIAN MAGNETOTELLURIC INVERSION USING METHYLENE BLUE STRUCTURAL PRIORS FOR IMAGING SHALLOW CONDUCTORS IN GEOTHERMAL FIELDS

Geophysics ◽  
2021 ◽  
pp. 1-66
Author(s):  
Alberto Ardid ◽  
David Dempsey ◽  
Edward Bertrand ◽  
Fabian Sepulveda ◽  
Flora Solon ◽  
...  
Keyword(s):  
Methylene Blue ◽  
Clay Content ◽  
3D Models ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Geothermal Fields ◽  
Geological Information ◽  
Resistivity Model ◽  
Irreducible Uncertainty ◽  
Clay Cap

In geothermal exploration, magnetotelluric (MT) data and inversion models are commonly used to image shallow conductors typically associated with the presence of an electrically conductive clay cap that overlies the main reservoir. However, these inversion models suffer from non-uniqueness and uncertainty, and the inclusion of useful geological information is still limited. We develop a Bayesian inversion method that integrates the electrical resistivity distribution from MT surveys with borehole methylene blue data (MeB), an indicator of conductive clay content. MeB data is used to inform structural priors for the MT Bayesian inversion that focus on inferring with uncertainty the shallow conductor boundary in geothermal fields. By incorporating borehole information, our inversion reduces non-uniqueness and then explicitly represents the irreducible uncertainty as estimated depth intervals for the conductor boundary. We use Markov chain Monte Carlo (McMC) and a one-dimensional three-layer resistivity model to accelerate the Bayesian inversion of the MT signal beneath each station. Then, inferred conductor boundary distributions are interpolated to construct pseudo-2D/3D models of the uncertain conductor geometry. We compared our approach against a deterministic MT inversion software on synthetic and field examples and showed good performance in estimating the depth to the bottom of the conductor, a valuable target in geothermal reservoir exploration.

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