Building Complex Synthetic Models to Evaluate Acquisition Geometries and Velocity Inversion Technologies

Jones, Charles E.; Edgar, J.A.; Selvage, J.I.; Crook, H.

doi:10.3997/2214-4609.20148575

Cited by 11 publications

(3 citation statements)

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“…In order to generate realistic time-lapse data and training sets for the automatic leakage classifier, we follow the workflow summarized in Figure 1. In this approach, use is made of proxy models for seismic properties derived from real 3D imaged seismic and well data [Jones et al, 2012]. With rock physics, these seismic models are converted to fluid-flow models that serve as input to two-phase flow simulations.…”

Section: Numerical Case Study: Blunt Sandstone In the Southern North Seamentioning

confidence: 99%

“…To arrive at the fluid-flow models, we consider 2D subsets of the 3D Compass model [Jones et al, 2012] and convert these seismic models to fluid-flow properties (see Figure 1 (b)) by assuming a linear relationship between compressional wavespeed and permeability in each stratigraphic section. For further details on the conversion of compressional wavespeed and density to permeability and porosity, we refer to empirical relationships reported in Klimentos [1991].…”

Section: Proxy Seismic and Fluid-flow Modelsmentioning

confidence: 99%

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De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Yin¹,

Erdinc²,

Gahlot³

et al. 2022

Preprint

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Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data and with a reasonable accuracy.

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Section: Numerical Case Study: Blunt Sandstone In the Southern North Seamentioning

confidence: 99%

Section: Proxy Seismic and Fluid-flow Modelsmentioning

confidence: 99%

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Yin¹,

Erdinc²,

Gahlot³

et al. 2022

Preprint

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show abstract

“…To mimic the complexities of field data, noisy shot data-generated with nonlinear forward modeling on a 2D subset of the Compass model [107]-is used as input to the proposed imaging scheme. We select the synthetic Compass model because it contains realistic heterogeneity derived from both seismic and well data collected in the North Sea [107]. Aside from a low signal-to-noise ratio of −9.17 dB, this example is affected by linearization errors.…”

Section: Uncertainty Due To Noise and Uncertain Control Points (Case 2)mentioning

confidence: 99%

Deep Bayesian inference for seismic imaging with tasks

Siahkoohi¹,

Rizzuti²,

Herrmann³

2021

Preprint

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We propose to use techniques from Bayesian inference and deep neural networks to translate uncertainty in seismic imaging to uncertainty in tasks performed on the image, such as horizon tracking. Seismic imaging is an ill-posed inverse problem because of unavoidable bandwidth and aperture limitations, which that is hampered by the presence of noise and linearization errors. Many regularization methods, such as transform-domain sparsity promotion, have been designed to deal with the adverse effects of these errors, however, these methods run the risk of biasing the solution and do not provide information on uncertainty in the image space and how this uncertainty impacts certain tasks on the image. A systematic approach is proposed to translate uncertainty due to noise in the data to confidence intervals of automatically tracked horizons in the image. The uncertainty is characterized by a convolutional neural network (CNN) and to assess these uncertainties, samples are drawn from the posterior distribution of the CNN weights, used to parameterize the image. Compared to traditional priors, in the literature it is argued that these CNNs introduce a flexible inductive bias that is a surprisingly good fit for many diverse domains in imaging. The method of stochastic gradient Langevin dynamics is employed to sample from the posterior distribution. This method is designed to handle large scale Bayesian inference problems with computationally expensive forward operators as in seismic imaging. Aside from offering a robust alternative to maximum a posteriori estimate that is prone to overfitting, access to these samples allow us to translate uncertainty in the image, due to noise in the data, to uncertainty on the tracked horizons. For instance, it admits estimates for the pointwise standard deviation on the image and for confidence intervals on its automatically tracked horizons.

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Parabolic dictionary learning for seismic wavefield reconstruction across the streamers

et al. 2018

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Dictionary learning (DL) methods are effective tools to automatically find a sparse representation of a data set. They train a set of basis vectors on the data to capture the morphology of the redundant signals. The basis vectors are called atoms, and the set is referred to as the dictionary. This dictionary can be used to represent the data in a sparse manner with a linear combination of a few of its atoms. In conventional DL, the atoms are unstructured and are only numerically defined over a grid that has the same sampling as the data. Consequently, the atoms are unknown away from this sampling grid, and a sparse representation of the data in the dictionary domain is not sufficient information to interpolate the data. To overcome this limitation, we have developed a DL method called parabolic DL, in which each learned atom is constrained to represent an elementary waveform that has a constant amplitude along a parabolic traveltime moveout. The parabolic structure is consistent with the physics inherent to the seismic wavefield and can be used to easily interpolate or extrapolate the atoms. Hence, we have developed a parabolic DL-based process to interpolate and regularize seismic data. Briefly, it consists of learning a parabolic dictionary from the data, finding a sparse representation of the data in the dictionary domain, interpolating the dictionary atoms over the desired grid, and, finally, taking the sparse representation of the data in the interpolated dictionary domain. We examine three characteristics of this method, i.e., the parabolic structure, the sparsity promotion, and the adaptation to the data, and we conclude that they strengthen robustness to noise and to aliasing and that they increase the accuracy of the interpolation. For both synthetic and field data sets, we have successful seismic wavefield reconstructions across the streamers for typical 3D acquisition geometries.

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Building Complex Synthetic Models to Evaluate Acquisition Geometries and Velocity Inversion Technologies

Cited by 11 publications

References 0 publications

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Deep Bayesian inference for seismic imaging with tasks

Parabolic dictionary learning for seismic wavefield reconstruction across the streamers

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