Subsurface sedimentary structure identification using deep learning: A review

C Zhan, Z Dai, Z Yang, X Zhang, Z Ma, HV Thanh… - Earth-Science …, 2023 - Elsevier
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …

Dynamic mesh optimisation for geothermal reservoir modelling

P Salinas, G Regnier, C Jacquemyn, CC Pain… - Geothermics, 2021 - Elsevier
Modelling geothermal reservoirs is challenging due to the large domain and wide range of
length-and time-scales of interest. Attempting to represent all scales using a fixed …

[HTML][HTML] Modelling saline intrusion using dynamic mesh optimization with parallel processing

A Hamzehloo, ML Bahlali, P Salinas… - Advances in Water …, 2022 - Elsevier
Saline intrusion (SI) in coastal aquifers is a global problem with the potential to contaminate
groundwater used by over a billion people. Numerical modelling of SI in coastal aquifers is a …

A Novel Hybrid Machine Learning Approach and Basin Modeling for Thermal Maturity Estimation of Source Rocks in Mandawa Basin, East Africa

CN Mkono, C Shen, AK Mulashani, MR Ngata… - Natural Resources …, 2024 - Springer
Basin modeling and thermal maturity estimation are crucial for understanding sedimentary
basin evolution and hydrocarbon potential. Assessing thermal maturity in the oil and gas …

Refined implicit characterization of engineering geology with uncertainties: a divide-and-conquer tactic-based approach

M Li, C Chen, H Liang, S Han, Q Ren, H Li - Bulletin of Engineering …, 2024 - Springer
In engineering geology, a reasonable assessment of the spatial distribution of uncertainty in
a region is vital in guiding research, saving money, and shortening the period. However, the …

Neural spline flow multi-constraint NURBS method for three-dimensional automatic geological modeling with multiple constraints

M Lyu, B Ren, X Wang, J Wang, J Yu, S Han - Computational Geosciences, 2023 - Springer
The strike and the dip angle are vital for describing the geometry of the rock formations.
However, in the interpolation and geological modeling, only the coordinates are considered; …

Addressing Configuration Uncertainty in Well Conditioning for a Rule-Based Model

O Ovanger, J Eidsvik, J Skauvold, R Hauge… - Mathematical …, 2024 - Springer
Rule-based reservoir models incorporate rules that mimic actual sediment deposition
processes for accurate representation of geological patterns of sediment accumulation …

Probabilistic reconstruction via machine-learning of the Po watershed aquifer system (Italy)

A Manzoni, GM Porta, L Guadagnini, A Guadagnini… - Hydrogeology …, 2023 - Springer
A machine-learning-based methodology is proposed to delineate the spatial distribution of
geomaterials across a large-scale three-dimensional subsurface system. The study area …

[HTML][HTML] Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements

E Kanin, A Garipova, S Boronin, V Vanovskiy… - Petroleum …, 2024 - Elsevier
We introduce a novel method for estimating the spatial distribution of absolute permeability
in oil reservoirs, consistent with well logging and well test measurements. The primary …

[PDF][PDF] A novel surface-based approach to represent aquifer heterogeneity in sedimentary formations

L Schorpp, J Straubhaar, P Renard - Authorea Preprints, 2024 - researchgate.net
Sedimentary formations that compose most aquifers are difficult to model as a result of the
nature of their deposition. Their formation generally involves multiple processes (alluvial …