FAST BAYESIAN INVERSION METHOD FOR THE GENERALIZED PETROPHYSICAL AND COMPOSITIONAL INTERPRETATION OF MULTIPLE WELL LOGS WITH UNCERTAINTY QUANTIFICATION

2019 ◽  
Author(s):  
Tianqi Deng ◽  
Joaquin Ambia ◽  
Carlos Torres-Verdín
Keyword(s):  
Uncertainty Quantification ◽  
Well Logs ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Compositional Interpretation

Bayesian inversion of well logs for petrophysical properties estimation

2017 ◽  
Author(s):  
Ruidong Qin ◽  
Heping Pan* ◽  
Peiqiang Zhao ◽  
Yutao Liu ◽  
Chengxiang Deng
Keyword(s):  
Well Logs ◽  
Bayesian Inversion ◽  
Petrophysical Properties

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.

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.

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.

scholarly journals Evaluation of a Straight-Ray Forward Model for Bayesian Inversion of Crosshole Ground Penetrating Radar Data

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 630 ◽  
Author(s):  
Hui Qin ◽  
Xiongyao Xie ◽  
Yu Tang
Keyword(s):  
Ground Penetrating Radar ◽  
Computational Cost ◽  
Measurement Data ◽  
Forward Model ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Model Parameters ◽  
Model Structure ◽  
Modeling Error ◽  
Ground Penetrating

Bayesian inversion of crosshole ground penetrating radar (GPR) data is capable of characterizing the subsurface dielectric properties and qualifying the associated uncertainties. Markov chain Monte Carlo (MCMC) simulations within the Bayesian inversion usually require thousands to millions of forward model evaluations for the parameters to hit their posterior distributions. Therefore, the CPU cost of the forward model is a key issue that influences the efficiency of the Bayesian inversion method. In this paper we implement a widely used straight-ray forward model within our Bayesian inversion framework. Based on a synthetic unit square relative permittivity model, we simulate the crosshole GPR first-arrival traveltime data using the finite-difference time-domain (FDTD) and straight-ray solver, respectively, and find that the straight-ray simulator runs 450 times faster than its FDTD counterpart, yet suffers from a modeling error that is more than 7 times larger. We also perform a series of numerical experiments to evaluate the performance of the straight-ray model within the Bayesian inversion framework. With modeling error disregarded, the inverted posterior models fit the measurement data nicely, yet converge to the wrong set of parameters at the expense of unreasonably large number of iterations. When the modeling error is accounted for, with a quarter of the computational burden, the main features of the true model can be identified from the posterior realizations although there still exist some unwanted artifacts. Finally, a smooth constraint on the model structure improves the inversion results considerably, to the extent that it enhances the inversion accuracy approximating to those of the FDTD model, and further reduces the CPU demand. Our results demonstrate that the use of the straight-ray forward model in the Bayesian inversion saves computational cost tremendously, and the modeling error correction together with the model structure constraint are the necessary amendments that ensure that the model parameters converge correctly.

From Seismic to Reservoir Properties With Geostatistical Inversion

1999 ◽  
Vol 2 (04) ◽  
pp. 334-340 ◽  
Author(s):  
Philippe Lamy ◽  
P.A. Swaby ◽  
P.S. Rowbotham ◽  
Olivier Dubrule ◽  
A. Haas
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
A Priori ◽  
Three Dimensional ◽  
Well Logs ◽  
Inversion Method ◽  
Inversion Process ◽  
Reservoir Properties ◽  
Stratigraphic Model ◽  
Geostatistical Inversion

Summary The methodology presented in this paper incorporates seismic data, geological knowledge and well logs to produce models of reservoir parameters and uncertainties associated with them. A three-dimensional (3D) seismic dataset is inverted within a geological and stratigraphic model using the geostatistical inversion technique. Several reservoir-scale acoustic impedance blocks are obtained and quantification of uncertainty is determined by computing statistics on these 3D blocks. Combining these statistics with the kriging of the reservoir parameter well logs allows the transformation of impedances into reservoir parameters. This combination is similar to performing a collocated cokriging of the acoustic impedances. Introduction Our geostatistical inversion approach is used to invert seismic traces within a geological and stratigraphic model. At each seismic trace location, a large number of acoustic impedance (AI) traces are generated by conditional simulation, and a local objective function is minimized to find the trace that best fits the actual seismic trace. Several three-dimensional (3D) AI realizations are obtained, all of which are constrained by both the well logs and seismic data. Statistics are then computed in each stratigraphic cell of the 3D results to quantify the nonuniqueness of the solution and to summarize the information provided by individual realizations. Finally, AI are transformed into other reservoir parameters such as Vshale through a statistical petrophysical relationship. This transformation is used to map Vshale between wells, by combining information derived from Vshale logs with information derived from AI blocks. The final block(s) can then be mapped from the time to the depth domain and used for building the flow simulation models or for defining reservoir characterization maps (e.g., net to gross, hydrocarbon pore volume). We illustrate the geostatistical inversion method with results from an actual case study. The construction of the a-priori model in time, the inversion, and the final reservoir parameters in depth are described. These results show the benefit of a multidisciplinary approach, and illustrate how the geostatistical inversion method provides clear quantification of uncertainties affecting the modeling of reservoir properties between wells. Methodology The Geostatistical Inversion Approach. This methodology was introduced by Bortoli et al.1 and Haas and Dubrule.2 It is also discussed in Dubrule et al.3 and Rowbotham et al.4 Its application on a synthetic case is described in Dubrule et al.5 A brief review of the method will be presented here, emphasizing how seismic data and well logs are incorporated into the inversion process. The first step is to build a geological model of the reservoir in seismic time. Surfaces are derived from sets of picks defining the interpreted seismic. These surfaces are important sincethey delineate the main layers of the reservoir and, as we will see below, the statistical model associated with these layers, andthey control the 3D stratigraphic grid construction. The structure of this grid (onlap, eroded, or proportional) depends on the geological context. The maximum vertical discretization may be higher than that of the seismic, typically from 1 to 4 milliseconds. The horizontal discretization is equal to the number of seismic traces to invert in each direction (one trace per cell in map view). Raw AI logs at the wells have to be located within this stratigraphic grid since they will be used as conditioning data during the inversion process. It is essential that well logs should be properly calibrated with the seismic. This implies that a representative seismic wavelet has been matched to the wells, by comparing the convolved reflectivity well log response with the seismic response at the same location. This issue is described more fully in Rowbotham et al.4 Geostatistical parameters are determined by using both the wells and seismic data. Lateral variograms are computed from the seismic mapped into the stratigraphic grid. Well logs are used to both give an a priori model (AI mean and standard deviation) per stratum and to compute vertical variograms. The geostatistical inversion process can then be started. A random path is followed by the simulation procedure, and at each randomly drawn trace location AI trace values can be generated by sequential Gaussian simulation (SGS). A large number of AI traces are generated at the same location and the corresponding reflectivities are calculated. After convolution with the wavelet, the AI trace that leads to the best fit with the actual seismic is kept and merged with the wells and the previously simulated AI traces. The 3D block is therefore filled sequentially, trace after trace (see Fig. 1). It is possible to ignore the seismic data in the simulation process by generating only one trace at any (X, Y) location and automatically keeping it as "the best one." In this case, realizations are only constrained by the wells and the geostatistical model (a-priori parameters and variograms).

scholarly journals European emissions of HCFC-22 based on eleven years of high frequency atmospheric measurements and a Bayesian inversion method

2015 ◽  
Vol 112 ◽  
pp. 196-207 ◽  
Author(s):  
F. Graziosi ◽  
J. Arduini ◽  
F. Furlani ◽  
U. Giostra ◽  
L.J.M. Kuijpers ◽  
...  
Keyword(s):  
High Frequency ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Atmospheric Measurements
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