A deep learning-aided approach for estimating field permeability map by fusing well logs, well tests, and seismic data; Petroleum; Vol. 11, iss. 6

A deep learning-aided approach for estimating field permeability map by fusing well logs, well tests, and seismic data; Petroleum; Vol. 11, iss. 6

Bibliographic Details
Parent link:Petroleum.— .— Amsterdam: Elsevier Science Publishing Company Inc.
Vol. 11, iss. 6.— 2026.— P. 813-824
Other Authors: Shutov G. Grigoriy, Duplyakov V. Viktor, Davoodi Sh. Shadfar, Morozov A. Anton, Popkov D. Dmitriy, Pavlenko K. Kirill, Vainshtein A. Albert, Kotezhekov V. Viktor, Kaygorodov S. Sergey, Belozerov B. Boris, Khasanov M. Mars, Vanovskiy V. Vladimir, Osiptsov A. Andrei, Burnaev E. Evgeny
Summary:Title screen
Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and, therefore, designing effective recovery strategies. This problem, however, remains challenging, as it requires the integration of various data sources by experts from different disciplines. Moreover, there are no sources to provide direct information about the inter-well space. In this work, a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area. This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources: well logs, well tests, and seismics. A convolutional neural network is developed to process seismic data and then incorporate it with other sources. A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct an adequate permeability map. The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia. The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells, and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data
Текстовый файл
AM_Agreement
Language:English
Published: 2026
Subjects:
электронный ресурс
труды учёных ТПУ
Data fusion
Permeability
Convolutional neural network
Seismic
Kernel regression
Online Access:https://doi.org/10.1016/j.petlm.2025.11.005
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=687217

Similar Items

Similar Items