Characterizing fluid contacts by joint inversion of seismic P-wave impedance and velocity

Ali, Amjad; Al-Shuhail, Abdullatif A.

doi:10.1007/s13202-017-0394-3

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…However, FWI suffers from ill-posedness, cycle-skipping, and high computational cost because of the nonlinearity of the inverse problem (Virieux and Operto, 2009;Feng and Schuster, 2019;Wu and Lin, 2019;Zhang and Alkhalifah, 2020;Feng et al, 2021;Zhang and Gao, 2021). In addition, FWI cannot directly estimate the CO 2 saturation, though empirical and statistical petrophysics relationships have to be employed as a post inversion step to reconstruct the saturation distribution from the inverted velocity field (Grana and Rossa, 2010;Ali and Al-Shuhail, 2018).…”

Section: Introductionmentioning

confidence: 99%

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

et al. 2022

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In carbon capture and sequestration, developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction accuracy, which hinders their application to time-lapse monitoring of CO2 sequestration. We propose an efficient “hybrid” time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is validated on a synthetic CO2-sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO2 movement in the reservoir. The developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.

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Section: Introductionmentioning

confidence: 99%

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

et al. 2022

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

Liu

Feng

Tsvankin

et al. 2022

Second International Meeting for Applied Geoscience &Amp; Energy

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In carbon capture and sequestration (CCS), developing rapid and effective imaging techniques is crucial for real-time monitoring of the spatial and temporal dynamics of CO 2 propagation during/after injection. With continuing improvements in computational power and data storage, data-driven techniques based on machine learning (ML) have been effectively applied to seismic inverse problems. In particular, ML helps alleviate the ill-posedness and high computational cost of full-waveform inversion (FWI). However, such data-driven inversion techniques require massive high-quality training data sets to ensure prediction 1 accuracy, which hinders their application to time-lapse monitoring of CO 2 sequestration.We propose an efficient "hybrid" time-lapse workflow that combines physics-based FWI and data-driven ML inversion. The scarcity of the available training data is addressed by developing a new data-generation technique with physics constraints. The method is validated on a synthetic CO 2 -sequestration model based on the Kimberlina storage reservoir in California. The proposed approach is shown to synthesize a large volume of high-quality, physically realistic training data, which is critically important in accurately characterizing the CO 2 movement in the reservoir. The developed hybrid methodology can also simultaneously predict the variations in velocity and saturation and achieve high spatial resolution in the presence of realistic noise in the data.

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Characterization of well logs using K-mean cluster analysis

Ali

Chen

2020

J Petrol Explor Prod Technol

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The identification process of different lithologies, hydrocarbons, and water-saturated zones in oil and gas industries involves petrophysical studies that are carried out by geoscientists using different software packages. This study aims to propose a method by integrating mean cluster analysis and well logs to identify dominant lithologies, pore fluids, and fluids contact. For this purpose, initially, K-mean cluster analysis is applied to density log and P-wave velocity data of three wells in order to group them into different clusters. Based on centroids of each cluster, different lithologies have been identified. The density log equation has been utilized to compute the porosity of each cluster, and the mean of each density log cluster is used as matrix density. Next, sonic log equation has been inverted to compute the fluid velocity and the mean of each P-wave velocity cluster is used as matrix velocity. For the fluid density, sonic and density log equations are jointly inverted to compute the fluid velocity of each cluster. The fluid bulk modulus and acoustic impedance are computed using fluid density and velocity. Based on the results of K-mean cluster analysis, different lithologies (shale, sandstone, and limestone) have been recognized successfully. In well-1, hydrocarbon and water-saturated zones are successfully identified and fluids contact has been established in the zone of interest. However, well-2 and well-3 did not show any indications of the presence of hydrocarbon in the respective zones.

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Characterizing fluid contacts by joint inversion of seismic P-wave impedance and velocity

Cited by 5 publications

References 14 publications

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

Joint physics-based and data-driven time-lapse seismic inversion: Mitigating data scarcity

Characterization of well logs using K-mean cluster analysis

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