Productivity Parameters Prediction on Maps Using Neural Networks

S.V. Egorov; I.I. Priezzhev; A.E. Schelkunov; E.A. Gladkov

doi:10.3997/2214-4609.201702226

oa Productivity Parameters Prediction on Maps Using Neural Networks
Authors S.V. Egorov¹, I.I. Priezzhev², A.E. Schelkunov³, E.A. Gladkov⁴
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Affiliations: ¹ MIMGO ² IPLAB LLC ³ OOO Pyrgeophisika ⁴ OOO Nord-Imperial, FGBOY VPO NI TPY
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, Geomodel 2017, Sep 2017, Volume 2017, p.1 - 5
DOI: https://doi.org/10.3997/2214-4609.201702226

Abstract

Summary

The presentation shows the technique and examples for predicting the oil and gas productivity parameters on the map on the basis of a deep neural network with hybrid training and Tikhonov regularization. The results of predicting the effective thickness in continental facies of Western Siberia are shown. The results of comparison between prediction maps obtained by the neural network technique and multidimensional regression are also shown. The advantages of a neural network are efficiency, higher resolution and better correlation coefficient with well data.

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2017-09-11

2024-04-24

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References

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Productivity Parameters Prediction on Maps Using Neural Networks

Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201702226

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oa Productivity Parameters Prediction on Maps Using Neural Networks

Abstract

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The natural combination of full and image‐based waveform inversion

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

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