Functional Estimator For Reservoir Proxy Models Made Scalable Through A Big Data Platform

M. Piantanida; A. Amendola; G. Formato

doi:10.3997/2214-4609.201803028

Functional Estimator For Reservoir Proxy Models Made Scalable Through A Big Data Platform
Authors M. Piantanida¹, A. Amendola¹, G. Formato¹
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Affiliations: ¹ Eni S.p.A.
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, First EAGE/PESGB Workshop Machine Learning, Nov 2018, Volume 2018, p.1 - 3
DOI: https://doi.org/10.3997/2214-4609.201803028

Abstract

Summary

The abstract documents how a Big Data Analytics platform allowed to implement a complex functional estimator of a reservoir proxy model, involving complex machine learning operations on dynamic reservoir models, so that it can scale up to the size of realistic reservoir models.

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2018-11-30

2024-04-20

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References

F.Bottazzi, E.Della Rossa
, A Functional Data Analysis Approach to Surrogate Modeling in Reservoir and Geomechanics Uncertainty Quantification, Math geoscience, 11May2017
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Functional Estimator For Reservoir Proxy Models Made Scalable Through A Big Data Platform

Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201803028

/content/papers/10.3997/2214-4609.201803028

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Functional Estimator For Reservoir Proxy Models Made Scalable Through A Big Data Platform

Abstract

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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