Fluid Prediction on Seismic Data Using Different Machine Learning Methods

V Kalashnikova; E Karaseva; R Overas; T.R Sharafutdinov

doi:10.3997/2214-4609.202150051

Fluid Prediction on Seismic Data Using Different Machine Learning Methods
Authors V Kalashnikova¹, E Karaseva³, R Overas¹, T.R Sharafutdinov²
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Affiliations: ¹ «PreStack Solutions-Geo» AS ² LLC «PetroTrace Services» ³ Independent consultant
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, Tyumen 2021, Mar 2021, Volume 2021, p.1 - 5
DOI: https://doi.org/10.3997/2214-4609.202150051

Abstract

Summary

Authors have investigated possibilities to predict the type of fluid from stacked seismic data using machine learning algorithms. Despite the small data sets of only four wells, the prediction shows promising results. Firstly authors investigated prediction on the wells: using wells-logs sets as learning and recognition. Then, authors used well-logs data, synthetic seismic trace and its derivatives for learning, and seismic and its derivatives for recognition.

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2021-03-22

2024-04-29

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References

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Fluid Prediction on Seismic Data Using Different Machine Learning Methods

Conference Proceedings 2021, 1 (2021); https://doi.org/10.3997/2214-4609.202150051

/content/papers/10.3997/2214-4609.202150051

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Fluid Prediction on Seismic Data Using Different Machine Learning Methods

Abstract

From This Site

Most Read This Month

Most Cited This Month Most Cited RSS feed

The natural combination of full and image‐based waveform inversion

Poststack diffraction imaging using reverse‐time migration

Characterizing the effect of elastic interactions on the effective elastic properties of porous, cracked rocks

Fracture detection by Gaussian beam imaging of seismic data and image spectrum analysis

Laboratory measurements of guided‐wave propagation within a fluid‐saturated fracture