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We propose to apply a transdimensional inversion algorithm, reversible-jump Markov chain Monte Carlo (rjMCMC), to seismic waveform inversion to characterize reservoir impedance and estimate uncertainty using post-stack data. This method can help to automatically determine a proper parameterization, specifically an optimal number of layers for a given data set and earth structure. The rjMCMC can also enhance uncertainty estimation since its transdimensional sampler can prevent a biased sampling of model space, including the number of unknowns. An ensemble of solutions with different parameterizations can statistically reduce the bias for parameter estimation and uncertainty quantification. Our results show that the inversion uncertainty, which includes uncertainty in both properties and their locations, is related to the contrast in properties across an interface. That is, there is a trade-off between property uncertainty and location uncertainty. A larger discontinuity will cause more uncertainty in model property values at the location of the interface, but less uncertainty in its location. Therefore, we propose to use the inversion uncertainty as a novel attribute to facilitate delineation of subsurface reflectors and quantify the magnitude of discontinuities.
Presentation Date: Wednesday, October 19, 2016
Start Time: 8:50:00 AM
Presentation Type: ORAL
Noei, Emad Ghaleh (Dept. of Geomatics Engineering University of Calgary, Canada) | Dettmer, Jan (Dept. of Geoscience, University of Calgary, Canada) | Ali, Mohammed (Dept. of Earth Sciences, Khalifa University, UAE) | Lee, Gyoo Ho (Korea Gas Corporation, Korea) | Kim, Jeong Woo (Dept. of Geomatics Engineering University of Calgary, Canada)
Abstract This work investigates nonlinear inversion of gravity data to infer Infracambrian Hormuz salt structures offshore Abu Dhabi, UAE. A Bayesian approach with a trans-dimensional parametrization of the subsurface is applied that does not require regularization, resulting in more objective inversion results. The trans-dimensional parametrizations discretize the subsurface structure including the salt dome by an irregular grid of Voronoi cells. Both the number of cells and the cell coordinates are unknown parameters estimated from gravity data. The density contrast of the salt structures is assumed as known. The solution in Bayesian inversion is given by a large ensemble of parameter sets. Here, the trans-dimensional ensemble is obtained with the reversible-jump Markov chain Monte Carlo (rjMCMC) algorithm. Residual errors are parametrized by a full covariance matrix, which is estimated and updated as part of an iterative inversion scheme. Efficient rjMCMC sampling is achieved with parallel tempering. Inversion of airborne gravity anomalies illustrates well-defined Infracambrian Hormuz salt structures offshore Abu Dhabi, where the irregular grid spatially adapts to the data information and without the need to impose explicit regularization or fixed grids. Uncertainty estimates highlight salt dome extent. This study provides new insight into the existence and shape of oil reservoirs associated with the underlying salt structures.
Biswas, Reetam (University of Texas at Austin, and BP) | Arnulf, Adrien F. (University of Texas at Austin) | Sen, Mrinal K. (University of Texas at Austin) | Datta, Debanjan (University of Texas at Austin, and Shell International Exploration and Production) | Zhao, Zeyu (University of Texas at Austin) | Mishra, Pankaj K. (University of Texas at Austin) | Jaysaval, Piyoosh (University of Texas at Austin, and Pacific Northwest National Laboratory)
Full Waveform Inversion (FWI) has become a powerful tool to generate high-resolution subsurface velocity models. FWI attempts to solve a non-linear and non-unique inverse problem, and is traditionally based on a local optimization technique. As a result, it can easily get stuck in a local minimum. To mitigate this deleterious effect, FWI requires a good starting model, which should be close enough to the optimal model to properly converge to the global minimum. Here, we investigate a two-step approach for solving this problem. In the first step, we generate a starting model for FWI, that includes the low-wavenumber information, from first-arrival traveltime tomography of downward extrapolated streamer data. We solve the tomography problem using a trans-dimensional approach, based on a Bayesian framework. The number of model parameters is treated as a variable, similar to the P-wave velocity information. We use an adaptive cloud of nuclei points and Voronoi cells to represent our 2D velocity model. We use Reversible Jump Markov Chain Monte Carlo (RJMCMC) to sample models from a variable dimensional model space and obtain an optimum starting model for local elastic FWI. We also estimate uncertainty in our tomography derived model. We solve for the Eikonal equation using a shortest path method for ray tracing in tomography and we solve the elastic wave equation using a time-domain finite-difference method in FWI. To compute the gradient we used the adjoint method. We demonstrate our algorithm on a real 2-D seismic streamer dataset from Axial Seamount, which is the most volcanically active site of the northeastern Pacific. We ran 17 Markov chains with different starting number of nuclei and convergence for all chains was attained in about 1000 iterations. Marginal posterior density plots of velocity models demonstrate uncertainty in the obtained starting velocity models. We then ran a local FWI using the combined result from all chains. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 2:40 PM Location: 362A Presentation Type: Oral
Ghalenoei, Emad (Dept. of Geomatics Engineering University of Calgary, CANADA) | Dettmer, Jan (Dept. of Geoscience, University of Calgary, CANADA) | Ali, Mohammed (Dept. of Earth Sciences, Khalifa University, UAE) | Lee, Gyoo Ho (Korea Gas Corporation, KOREA) | Kim, Jeong Woo (Dept. of Geomatics Engineering University of Calgary, CANADA)
This study demonstrates a nonlinear inversion of gravity data to image the Infracambrian Hormuz salt structure of Ghasha oilfield offshore Abu Dhabi, UAE. Bayesian approach with a trans-dimensional (trans-D) parametrization of the subsurface model is carried out without any constraints and regularization, bringing more objective inversion outputs. The trans-D parametrizations represent a discretization of the salt structure by an irregular grid of Voronoi nodes. Both the number and positions of Voronoi nodes are unknown parameters estimated from gravity data. A parallel tempering technique is applied to achieve an efficient rjMCMC sampling and increase acceptance rates. We apply a predefined basement from depth around 8 km to 10 km in the model space based on prior geological information. Moreover, we define a depth of 3.5 km as the lower bound of depth in our model space. As a result, Voronoi nodes place into the truncated model space that does not include the basement and depths shallower than 3.5 km. This discretization results in a salt model consistent with the geological information in Ghasha. This study also provides new insight into the existence and shape of oil reservoirs associated with the underlying Ghasha by introducing a piercing and a non-piercing salt structure.