Lithofacies and fluid prediction of a sandstone reservoir using pre-stack inversion and non-parametric statistical classification: A case study

Mahadasu, Nagendra Babu; Ambati, Venkatesh; Nair, R.R.

doi:10.1007/s12040-021-01792-y

Cited by 5 publications

(1 citation statement)

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“…For instance, the impedance values for hydrocarbon-bearing sands typically fall within the range of 17,500 to 21,500 (ft/s)*(g/cc), while water-bearing sands are found in the range of 22,000 to 24,500 (ft/s)*(g/cc). Shale encompasses values spanning from 24,500 to 27,500 (ft/s)*(g/cc) [56,58]. However, we would like to point out that this categorization is beyond the primary scope of this study, where we treated AI as individual data samples.…”

Section: Seismic Data Pre-processingmentioning

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: Seismic Data Pre-processingmentioning

confidence: 99%