A deep learning-aided approach for estimating field permeability map by fusing well logs, well tests, and seismic data

Abstract

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.

Keywords

Data fusion / Permeability / Convolutional neural network / Seismic / Kernel regression

CRediT authorship contribution statement

Grigoriy Shutov: Writing-review & editing, Writing-original draft, Visualization, Software, Investigation, Formal analysis. Viktor Duplyakov: Writing-review & editing, Writing-original draft, Visualization, Software, Investigation, Formal analysis. Shadfar Davoodi: Writing-review & editing, Writing-original draft, Visualization, Validation, Formal analysis. Anton Morozov: Writing-original draft, Investigation. Dmitriy Popkov: Writing-original draft, Investigation. Kirill Pavlenko: Writing-original draft, Investigation. Albert Vainshtein: Supervision, Project administration. Viktor Kotezhekov: Supervision, Resources, Data curation. Sergey Kaygorodov: Resources, Data curation. Boris Belozerov: Validation, Supervision, Project administration. Mars M. Khasanov: Supervision, Project administration, Funding acquisition. Vladimir Vanovskiy: Writing-review & editing, Project administration, Methodology, Conceptualization. Andrei Osiptsov: Writing-review & editing, Supervision, Funding acquisition. Evgeny Burnaev: Writing-review & editing, Supervision, Funding acquisition.

Funding

The work was supported by the grant for research centers in the field of AI provided by the Ministry of Economic Development of the Russian Federation in accordance with the agreement 000000C313925P4F0002 and the agreement with Skoltech N◦ 139-10-2025-033.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors gratefully appreciate insightful discussions with experts from “LLC Gazpromneft-STC” and thank the Association “Artificial intelligence in industry” for providing a platform for such discussions.

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