A multi-scale blocking moving window algorithm for geostatistical seismic inversion

Hu, Xun; JiaGen, Hou,; Yin, Yanshu; Liu, Yuming; Wang, Lixin; Kang, Qiangqiang; Hou, Mingqiu

doi:10.1016/j.cageo.2023.105313

Cited by 5 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Strategies like integrating simulated annealing can further increase acceptance rates. Typically, an acceptance rate of 25%–50% for suggested models is considered computationally efficient (Gelman et al., 1996; Hu et al., 2023; Laloy et al., 2016). After a burn‐in period of approximately 1,400 iterations, posterior models were sampled.…”

Section: History Matching Using the Pre‐trained Generatormentioning

confidence: 99%

Stochastic Modeling of Thin Mud Drapes Inside Point Bar Reservoirs With ALLUVSIM‐GANSim

Hu,

Song,

Hou

et al. 2024

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Modeling inclined fine‐scale mud drapes inside point bars, deposited on accretion surfaces during stages of low energy or slack water, is critical to modeling fluid flow in complex sedimentary environments (e.g., fluvial and turbidity flows). These features have been modeled using deterministic or geostatistical modeling tools (e.g., object‐, event‐, and pixel‐based). However, this is a non‐trivial task due to the need to preserve geological realism (e.g., connectivity within sedimentary features and facies hierarchy), while being able to condition the generated models to point data (e.g., well data). Generative Adversarial Networks (GAN) have been successfully applied to reproduce several large‐scale scenarios (e.g., braided rivers and carbonate reservoirs), yet their potential for capturing small‐scale and hierarchical features remains largely unexplored. Here, we propose a geo‐modeling workflow for fast modeling of small‐scale conditional mud drapes based on ALLUVSIM and GANSim. Initially, improved ALLUVSIM produces realistic unconditional models of mud drapes along accretionary surfaces, serving as GAN training data. GANSim is then employed to achieve conditioning to well data and probability maps derived from geophysical modeling. Finally, temporal pressure data observed in wells are further conditioned via a Markov chain Monte Carlo sampling method. The proposed geo‐modeling workflow is validated in a two‐dimensional synthetic example as the pre‐trained generator extracts mud‐drapes‐features and generates multiple facies realizations conditioned to diverse information. A field application example in a modern meandering river verifies the effectiveness and practicability of the proposed workflow in real case application examples. The application examples illustrate the potential of the proposed method to predict mud drapes inside point bar reservoirs.

show abstract

Section: History Matching Using the Pre‐trained Generatormentioning

confidence: 99%

Stochastic Modeling of Thin Mud Drapes Inside Point Bar Reservoirs With ALLUVSIM‐GANSim

Hu,

Song,

Hou

et al. 2024

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lang and Grana (2017), Fjeldstad and Grana (2018) and de Figueiredo et al (2019) proposed a multimodal distribution assumption, combining lithology division with inversion, constructing a Gaussian mixture posterior probability distribution and reducing calculation costs. Hu et al (2023) proposed a multi-scale block moving window algorithm which can obtain higher quality results more efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al. (2023) proposed a multi‐scale block moving window algorithm which can obtain higher quality results more efficiently.…”

Section: Introductionmentioning

confidence: 99%

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

Su,

Xu,

Chen

et al. 2024

Geophysical Prospecting

View full text Add to dashboard Cite

Geostatistical seismic inversion is an important method for establishing high‐resolution reservoir parameter models. There is no accurate representation method for reservoir structural features, and prior information about structural features cannot be incorporated into geostatistical inversion. Based on the assumption of the sparsity of stratigraphic sedimentary features, the same type of structural feature is used to represent the sedimentary pattern of reservoirs within the same facies. Different sparse representation patterns are used to represent the differences in sedimentary patterns between facies. Although changes in depositional environment might result in the multi‐scale characteristics of geological structures for varying sedimentary rhythms, this paper proposes a facies‐constrained geostatistical inversion method based on multi‐scale sparse representation to better accommodate such situation. Using the method of sparse representation combined with wavelet transform, the multi‐scale sedimentary structural features of reservoirs are learned from well‐logging data. Seismic facies and multi‐scale features are used as prior information for geostatistical inversion. Further, the likelihood function is constructed using seismic data to obtain the posterior probability distribution of reservoir parameters. Finally, the accurate inversion result is obtained by using multi‐scale sparse representation as a constraint in the posterior probability distribution of reservoir parameters. Compared with conventional geostatistical methods, this algorithm can better match the structural features of reservoir parameters with varying geological conditions. Field data tests have shown the effectiveness of this method in improving the accuracy and resolution of reservoir parameter structural features.

show abstract

Improving ellipse fitting via multi-scale smoothing and key-point searching

Chen,

Qian,

Zhao

et al. 2024

Pattern Recognition

View full text Add to dashboard Cite

A multi-scale blocking moving window algorithm for geostatistical seismic inversion

Cited by 5 publications

References 50 publications

Stochastic Modeling of Thin Mud Drapes Inside Point Bar Reservoirs With ALLUVSIM‐GANSim

Stochastic Modeling of Thin Mud Drapes Inside Point Bar Reservoirs With ALLUVSIM‐GANSim

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

Improving ellipse fitting via multi-scale smoothing and key-point searching

Contact Info

Product

Resources

About