Elastic full waveform inversion with probabilistic petrophysical clustering

Aragao, Odette; Sava, Paul

doi:10.1111/1365-2478.12910

Cited by 8 publications

(3 citation statements)

References 30 publications

(43 reference statements)

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“…Accurate determination of the seismic velocity model is a key factor affecting the imaging and interpretation effect of subsurface geology. Full waveform inversion (FWI) is an advanced velocity inversion method that can achieve a good inversion effect (Aragao & Sava, 2020; Huang & Schuster, 2018; B. Sun & Alkhalifah, 2020a).…”

Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on inversion network reparameterized velocity

Jiang

Wang

Ren

et al. 2022

Geophysical Prospecting

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Seismic velocity plays an important role in imaging and identifying underground geology. Conventional seismic velocity inversion methods, like full waveform inversion, directly update the velocity model based on the misfit between the observed and synthetic data. However, seismic velocity inversion is a highly nonlinear process, and the inversion effect greatly relies on the initial inversion model. In this paper, we propose a novel network‐domain full waveform inversion method. Different from the existing network‐domain full waveform inversion methods, which use random or fixed numbers as network input, we reparameterize the low‐dimensional acoustic velocity model in a high‐dimensional inversion network parameter domain with seismic observed data as the network input. In this way, the physical information within the observed data can be directly encoded into the inversion parameters, leading to a better inversion effect than the current network‐domain full waveform inversion method. Moreover, comparison experiments on the Society of Exploration Geophysicists and the European Association of Geoscientists and Engineers Overthrust model and the Marmousi model show the advantages of the proposed method over conventional full waveform inversion from the aspects of inversion accuracy, robustness to noisy data, and more complex geological structures. These advantages may benefit from the fact that reparameterization within the inversion network domain can empower the inversion process with the regularization ability of denoising and mitigating the cycle‐skipping issue. In the end, the potential of the proposed method in terms of network initialization is further discussed.

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Section: Introductionmentioning

confidence: 99%

Full waveform inversion based on inversion network reparameterized velocity

Jiang

Wang

Ren

et al. 2022

Geophysical Prospecting

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“…Such inherent limitations of seismic data can be mitigated by integrating data from other geophysical surveys and disciplines. Using the Bayesian framework, we can complement seismic data with geologic information and rock-physics knowledge as we estimate the velocity, thus improving the consistency of the inverted model with the underlying geologic and lithologic assumptions (Curtis & Lomax 2001;Zunino et al 2015;Li et al 2016;Zhang et al 2018;Aragao & Sava 2020). Borehole data, which includes well logs, check shots, and vertical seismic profiles, show promising complementary information to seismic data to reduce the illposedness of the inverse problem (Asnaashari et al 2013;Wang et al 2013;Zhang & Alkhalifah 2019b).…”

mentioning

confidence: 99%

“…In a similar way, Singh et al (2019) constrain the inversion workflow using the prior model derived from the facies distribution and the available well logs. Aragao & Sava (2020) developed an elastic FWI algorithm constrained by petrophysical information extracted from well logs to guide the inversion toward realistic lithology. Zhang & Alkhalifah (2019b) improve the incorporation of facies information using deep neural networks.…”

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confidence: 99%

Deep-learning assisted regularized elastic full waveform inversion using the velocity distribution information from wells

Alkhalifah

Zhang

2021

Geophysical Journal International

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Summary Elastic full waveform inversion (EFWI) can, theoretically, give high-resolution estimates of the subsurface. However, in practice, the resolution and illumination of EFWI are limited by the bandwidth and aperture of seismic data. The often-present wells in developed fields as well as some exploratory regions can provide complementary information of the target area. We, thus, introduce a regularization technique, which combines the surface seismic and well log data, to help improve the resolution of EFWI. We use deep neural networks to learn the statistical relations between some selected features of the inverted model and the facies interpreted from well logs. The selected features are the means and variances of the inverted velocities defined within Gaussian windows. Using multiple fully-connected layers, we train our neural networks to identify the relation between the means and variances at the well location and those from the inverted model. The network is used to map the means and variances extracted from the well to the whole model domain. We then perform another EFWI in which we fit the predicted data to the observed ones as well as fit the model over a Gaussian window to the predicted means the variances. The tests on synthetic and real seismic data demonstrate that the proposed method can effectively improve the resolution and illumination of deep-buried reservoirs, which often encounter poor illumination from seismic data.

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Target-oriented time-lapse waveform inversion using deep learning-assisted regularization

Alkhalifah

Guo

2021

GEOPHYSICS

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Detection of the property changes in the reservoir during injection and production is important. However, the detection process is very challenging using surface seismic surveys because these property changes often induce subtle changes in the seismic signals. The quantitative evaluation of the subsurface property obtained by full waveform inversion (FWI) allows for better monitoring of these time-lapse changes. However, high-resolution inversion is usually accompanied with a large computational cost. Besides, the resolution of inversion is limited by the bandwidth and aperture of time-lapse seismic data. We apply a target-oriented strategy through seismic redatuming to reduce the computational cost by focusing our high-resolution delineation on a relatively small zone of interest. The redatuming technique generates time-lapse virtual data for the target-oriented inversion. Considering the injection and production wells are often present in the target zone, we can incorporate the well velocity information to the time-lapse inversion by using regularization to complement the resolution and illumination at the reservoir. We use a deep neural network (DNN) to learn the statistical relationship between the inverted model and the facies interpreted from well logs. The trained network is employed to map the property changes extracted from the wells to the target inversion domain. We then perform another time-lapse inversion, in which we fit the predicted data difference to the redatumed one from observation, as well as fit the model to the predicted velocity changes. The numerical results demonstrate that the proposed method is capable of inverting for the time-lapse property changes effectively in the target zone by incorporating the learned model information from well logs.

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Elastic full waveform inversion with probabilistic petrophysical clustering

Cited by 8 publications

References 30 publications

Full waveform inversion based on inversion network reparameterized velocity

Full waveform inversion based on inversion network reparameterized velocity

Deep-learning assisted regularized elastic full waveform inversion using the velocity distribution information from wells

Target-oriented time-lapse waveform inversion using deep learning-assisted regularization

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