Time-to-depth conversion

Toorn, Jaap Van der; Martínez, Gerardo Clemente; Hanson, Grant; Tariq, Hammad Hassan; Shalaby, Hesham; Molen, Matthijs van der; Shah, Zaheer Ali

doi:10.1016/b978-0-12-817236-0.00007-8

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Seismic data were organized as a volumetric cube, whereas well-log information consisted of rows with borehole petrophysical measurements. The integration process entailed combining the seismic and well-log data in the same domain through well-log-related details, such as the well-track and a time-depth transformation [57]. It is important to note that the time-depth transformation had been obtained in a previous analysis.…”

Section: Dataset Preparation: Integration and Alignmentmentioning

confidence: 99%

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Ek-Chacón,

Molino-Minero-Re,

Méndez-Monroy

et al. 2024

Applied Sciences

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A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

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Section: Dataset Preparation: Integration and Alignmentmentioning

confidence: 99%

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Ek-Chacón,

Molino-Minero-Re,

Méndez-Monroy

et al. 2024

Applied Sciences

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Study of Constrained Velocity Inversion of Seismic Data in North Sumatra Basin

Rubiyana

Nurcahya

2021

J. Eng. Technol. Sci.

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Constrained velocity inversion is an interval velocity analysis using an exponential asymptotically bounded velocity model, which can show a delineation match between the model and the velocity inversion functions. The constrained velocity inversion results were adequate for use as seismic velocity input data and produced a better seismic section than the root mean square velocity input data, indicated by clear continuity of the reflectors and the fault. Constrained velocity inversion applied to seismic data in the North Sumatra Basin showed a more rigorous seismic section compared to that from the root mean square velocity analysis.

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Time-to-depth conversion

Cited by 2 publications

References 6 publications

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Study of Constrained Velocity Inversion of Seismic Data in North Sumatra Basin

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