Hybrid geological modeling: Combining machine learning and multiple-point statistics

T Bai, P Tahmasebi - Computers & geosciences, 2020 - Elsevier
Accurately modeling and constructing a geologically realistic subsurface model remains an
outstanding problem as the morphology controls the flow behaviors. Particularly, one of the …

Stochastic pix2pix: A new machine learning method for geophysical and well conditioning of rule-based channel reservoir models

W Pan, C Torres-Verdín, MJ Pyrcz - Natural Resources Research, 2021 - Springer
Constructing subsurface models that accurately reproduce geological heterogeneity and
their associated uncertainty is critical to many geoscience and engineering applications. For …

Integrated modelling framework for enhancement history matching in fluvial channel sandstone reservoirs

HV Thanh, Y Sugai - Upstream Oil and Gas Technology, 2021 - Elsevier
Modelling lithofacies and petrophysical properties are the challenging processes at the
beginning of exploration and production of hydrocarbon reservoirs. However, the limited …

3D modeling of deepwater turbidite lobes: a review of the research status and progress

LF Zhang, M Pan, ZL Li - Petroleum Science, 2020 - Springer
Deepwater turbidite lobe reservoirs have massive hydrocarbon potential and represent one
of the most promising exploration targets for hydrocarbon industry. Key elements of turbidite …

Surface-based geological reservoir modelling using grid-free NURBS curves and surfaces

C Jacquemyn, MD Jackson, GJ Hampson - Mathematical Geosciences, 2019 - Springer
Building geometrically realistic representations of geological heterogeneity in reservoir
models is a challenging task that is limited by the inflexibility of pre-defined pillar or …

Hierarchical machine learning workflow for conditional and multiscale deep-water reservoir modeling

W Pan, H Jo, JE Santos, C Torres-Verdín… - AAPG …, 2022 - archives.datapages.com
Unconfined deep-water lobe deposits are among the most important targets in deep-water
oil field exploration and production. Accurate stochastic simulations of the sedimentary …

Conditioning surface-based geological models to well data using artificial neural networks

Z Titus, C Heaney, C Jacquemyn, P Salinas… - Computational …, 2022 - Springer
Surface-based modelling provides a computationally efficient approach for generating
geometrically realistic representations of heterogeneity in reservoir models. However …

[HTML][HTML] Reproduction of channel stacking patterns in geomodeling: Metrics and impact of the modeling strategy on reservoir flow behavior

E Scarpa, P Collon, I Panfilova, G Caumon - Marine and Petroleum Geology, 2025 - Elsevier
Channelized turbidite systems are often grouped into complexes and exhibit various
stacking patterns, which play a crucial role in controlling the connectivity between high …

Reconstruction of fluvial reservoirs using multiple-stage concurrent generative adversarial networks

T Zhang, X Ji, A Zhang - Computational Geosciences, 2021 - Springer
With the continuous depletion of oil and natural gas resources, the development of fluvial
reservoirs has gradually become a key research direction, which makes the reconstruction …

Combined inverse and forward numerical modelling for reconstruction of channel evolution and facies distributions in fluvial meander-belt deposits

M Parquer, N Yan, L Colombera, NP Mountney… - Marine and Petroleum …, 2020 - Elsevier
The sedimentary record of meandering rivers contains a diverse and complex set of
lithological heterogeneities, which impact natural resource management. Different methods …