Prestack simultaneous inversion of P-wave impedance and gas saturation using multi-task residual networks

Sang, Wenjing; Ding, Zhiqiang; Li, Mingxuan; Liu, Xiwu; Liu, Qian; Yuan, Sanyi

doi:10.1007/s11600-023-01132-2

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Artykuł - szczegóły

Czasopismo

Acta Geophysica

2024 | Vol. 72, no. 2 | 875--892

Tytuł artykułu

Prestack simultaneous inversion of P-wave impedance and gas saturation using multi-task residual networks

Autorzy

Sang, Wenjing , Ding, Zhiqiang , Li, Mingxuan , Liu, Xiwu , Liu, Qian , Yuan, Sanyi

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Warianty tytułu

Języki publikacji

Abstrakty

It is challenging to realize the gas saturation (GS) estimation via the integration of seismic data and more complementary data (e.g., elastic attributes) in a traditional physical framework. Machine learning, especially multi-task learning (MTL), provides an alternative way for the fuse of multiple information and simultaneous inversion of two or multiple reservoir parameters without model-driven limitations and interactive operators. To improve the estimation accuracy of GS, we propose the prestack simultaneous inversion of P-wave impedance (PI) and GS using the multi-task residual network (MT-ResNet). The designed MT-ResNet consists of two task-related subnets. The first subnet establishes the nonlinear links among low-frequency PI, prestack seismic data, and well-log derived PI. Furthermore, seismic data and the inverted PI via the first subnet are jointly entered into the second subnet and evolved into the well-log interpreted GS. A model based on measured petrophysical parameters associated with the field deep tight dolomite reservoir is used to test the proposed method. Tests on the synthetic data example and the field example demonstrate that the MT-ResNet can simultaneously estimate PI and GS models with the highest reliability, in comparison with single-task residual network (ST-ResNet) and the conventional seismic inversion and rock-physics equations-based method. And the MT-ResNet inverted PI can be utilized as complementary information for improving the prediction accuracy of MT-ResNet inverted GS. Our proposed MT-ResNet has the potential to guide the design of the MTL-based multiple reservoir parameters prediction and practical application.

Słowa kluczowe

uczenie się wielozadaniowe uczenie maszynowe

multitask learning machine learning gas saturation prediction P-wave impedance inversion pre-stack simultaneous inversion

Wydawca

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 2

Strony

875--892

Opis fizyczny

Bibliogr. 52 poz.

Twórcy

autor

Sang, Wenjing

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China, yuansy@cup.edu.cn
Sinopec Key Laboratory of Seismic Elastic Wave Technology, Beijing 100083, China
Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China
College of Geophysics, China University of Petroleum, Beijing, Beijing 102249, China

autor

Ding, Zhiqiang

College of Geophysics, China University of Petroleum, Beijing, Beijing 102249, China

autor

Li, Mingxuan

College of Geophysics, China University of Petroleum, Beijing, Beijing 102249, China

autor

Liu, Xiwu

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China
Sinopec Key Laboratory of Seismic Elastic Wave Technology, Beijing 100083, China
Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China

autor

Liu, Qian

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China
Sinopec Key Laboratory of Seismic Elastic Wave Technology, Beijing 100083, China
Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China

autor

Yuan, Sanyi

State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China
Sinopec Key Laboratory of Seismic Elastic Wave Technology, Beijing 100083, China
Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China
College of Geophysics, China University of Petroleum, Beijing, Beijing 102249, China

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Typ dokumentu

Bibliografia

Identyfikatory

DOI

10.1007/s11600-023-01132-2

Identyfikator YADDA

bwmeta1.element.baztech-54779eed-e9ad-4cde-b98e-f8c434dfd046