Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling

J. Mullins; Helena van der Vegt; J. Howell

doi:10.3997/2214-4609.201800772

Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling

J. Mullins, Helena van der Vegt, J. Howell

Applied Geology

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Reservoir models can be improved through the incorporation of process-based models as training images for simulation with MPS. Process-based models in this study are reported to be excellent sources of training images. When combined with knowledge of depositional stratigraphic information, delta complex architectural trends can be accurately reproduced. This approach is applicable to systems that are well constrained by knowledge of depositional processes that can be efficiently synthetized to generate an appropriate process model-derived training image.

Original language	English
Title of host publication	80th EAGE Conference and Exhibition 2018, 11-14 June, Copenhagen, Denmark
Number of pages	5
DOIs	https://doi.org/10.3997/2214-4609.201800772
Publication status	Published - 2018
Event	80th EAGE Conference and Exhibition 2018: Opportunities presented by the energy transition - Copenhagen, Denmark Duration: 11 Jun 2018 → 14 Jun 2018 Conference number: 80 https://events.eage.org/2018/EAGE%20Annual%202018

Conference

Conference	80th EAGE Conference and Exhibition 2018
Abbreviated title	EAGE 2018
Country/Territory	Denmark
City	Copenhagen
Period	11/06/18 → 14/06/18
Internet address	https://events.eage.org/2018/EAGE%20Annual%202018

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10.3997/2214-4609.201800772

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title = "Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling",

abstract = "Reservoir models can be improved through the incorporation of process-based models as training images for simulation with MPS. Process-based models in this study are reported to be excellent sources of training images. When combined with knowledge of depositional stratigraphic information, delta complex architectural trends can be accurately reproduced. This approach is applicable to systems that are well constrained by knowledge of depositional processes that can be efficiently synthetized to generate an appropriate process model-derived training image.",

author = "J. Mullins and {van der Vegt}, Helena and J. Howell",

year = "2018",

doi = "10.3997/2214-4609.201800772",

language = "English",

booktitle = "80th EAGE Conference and Exhibition 2018, 11-14 June, Copenhagen, Denmark",

note = "80th EAGE Conference and Exhibition 2018 : Opportunities presented by the energy transition, EAGE 2018 ; Conference date: 11-06-2018 Through 14-06-2018",

url = "https://events.eage.org/2018/EAGE%20Annual%202018",

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TY - GEN

T1 - Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling

AU - Mullins, J.

AU - van der Vegt, Helena

AU - Howell, J.

N1 - Conference code: 80

PY - 2018

Y1 - 2018

N2 - Reservoir models can be improved through the incorporation of process-based models as training images for simulation with MPS. Process-based models in this study are reported to be excellent sources of training images. When combined with knowledge of depositional stratigraphic information, delta complex architectural trends can be accurately reproduced. This approach is applicable to systems that are well constrained by knowledge of depositional processes that can be efficiently synthetized to generate an appropriate process model-derived training image.

AB - Reservoir models can be improved through the incorporation of process-based models as training images for simulation with MPS. Process-based models in this study are reported to be excellent sources of training images. When combined with knowledge of depositional stratigraphic information, delta complex architectural trends can be accurately reproduced. This approach is applicable to systems that are well constrained by knowledge of depositional processes that can be efficiently synthetized to generate an appropriate process model-derived training image.

U2 - 10.3997/2214-4609.201800772

DO - 10.3997/2214-4609.201800772

M3 - Conference contribution

BT - 80th EAGE Conference and Exhibition 2018, 11-14 June, Copenhagen, Denmark

T2 - 80th EAGE Conference and Exhibition 2018

Y2 - 11 June 2018 through 14 June 2018

ER -

Combining Multiple Point Geostatistics and Process-based Models for Improved Reservoir Modelling

Abstract

Conference

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