Výsledky vyhledávání - "Ehsan Zabihi Naeini"

A comprehensive study of calibration and uncertainty quantification for Bayesian convolutional neural networks — An application to seismic data

Autor: Lukas Mosser, Ehsan Zabihi Naeini

Publikováno v: GEOPHYSICS. 87:IM157-IM176

Deep neural networks offer numerous potential applications across geoscience; for example, one could argue that they are the state-of-the-art method for predicting faults in seismic data sets. In quantitative reservoir characterization workflows, it

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8d2cf092df9a1d2b881689041f899338
https://doi.org/10.1190/geo2021-0318.1

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Facies prediction with Bayesian inference: Application of supervised and semisupervised deep learning

Autor: Sagar Singh, Ilya Tsvankin, Ehsan Zabihi Naeini

Publikováno v: Interpretation. 10:T279-T290

Accurate delineation of geologic facies and determination of live fluids from seismic reflection data is crucial for reservoir characterization during petroleum exploration. Facies classification or fluid identification is often done manually by an e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::361834bdbebfc27b147d3e4fef2909ed
https://doi.org/10.1190/int-2021-0104.1

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Facies‐based full‐waveform inversion for anisotropic media: a North Sea case study

Autor: Ilya Tsvankin, Ehsan Zabihi Naeini, Sagar Singh

Publikováno v: Geophysical Prospecting. 69:1650-1663

Elastic full‐waveform inversion can increase the resolution of reservoir characterization using seismic data. However, full‐waveform inversion for realistic anisotropic media suffers from the trade‐offs between the medium parameters and strongl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::52a0389dd7524728ecce96fdf69f6110
https://doi.org/10.1111/1365-2478.13139

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Elastic FWI for orthorhombic media with lithologic constraints applied via machine learning

Autor: Ilya Tsvankin, Ehsan Zabihi Naeini, Sagar Singh

Publikováno v: GEOPHYSICS. 86:R589-R602

Full-waveform inversion (FWI) of 3D wide-azimuth data for elastic orthorhombic media suffers from parameter trade-offs which cannot be overcome without constraining the model-updating procedure. We present an FWI methodology that incorporates geologi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cc4137a558a6149fdb78ca86473f79ae
https://doi.org/10.1190/geo2020-0512.1

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Cloud computing in geoscience: Mysteries, miseries, and benefits

Autor: Eirik Larsen, Behzad Alaei, Ehsan Zabihi Naeini, D. Oikonomou

Publikováno v: Interpretation. 9:SA17-SA22

Cloud computing has become an integral part of our daily life and work. In the oil and gas industry, cloud computing is becoming increasingly attractive to experts, operators, and software companies. However, we believe that there is still some level

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::85f539b033ebbb783f07abdccee06bd9
https://doi.org/10.1190/int-2020-0103.1

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Full-waveform inversion with borehole constraints for elastic VTI media

Autor: Ehsan Zabihi Naeini, Ilya Tsvankin, Sagar Singh

Publikováno v: GEOPHYSICS. 85:R553-R563

The nonlinearity of full-waveform inversion (FWI) and parameter trade-offs can prevent convergence toward the actual model, especially for elastic anisotropic media. The problems with parameter updating become particularly severe if ultra-low-frequen

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::54d43f43d5a7b922d8a12cf2e82717e2
https://doi.org/10.1190/geo2019-0816.1

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Missed pay prediction — the advent of a new era

Autor: Daniel Stoddart, Ehsan Zabihi Naeini, D. Oikonomou, B. Alaei, Eirik Larsen

Publikováno v: First Break. 38:47-50

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9afd4cd6c28c5a9e1be5efe04b5fedbc
https://doi.org/10.3997/1365-2397.fb2020072

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Missing well log prediction using convolutional long short-term memory network

Autor: Nam Pham, Ehsan Zabihi Naeini, Xinming Wu

Publikováno v: GEOPHYSICS. 85:WA159-WA171

Reservoir characterization involves integration of different types of data to understand the subsurface rock properties. To incorporate multiple well log types into reservoir studies, estimating missing logs is an essential step. We have developed a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f451f7d7c190b761b4c0e58329f0e202
https://doi.org/10.1190/geo2019-0282.1

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