Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
Data‐worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework
Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …
applications. Data assimilation‐based identification frameworks can reasonably estimate …
Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II
A Samnioti, V Gaganis - Energies, 2023 - mdpi.com
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry,
with numerous applications which guide engineers in better decision making. The most …
with numerous applications which guide engineers in better decision making. The most …
A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage
Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon
dioxide (CO 2) plume migration under geologic uncertainties is a challenging problem in …
dioxide (CO 2) plume migration under geologic uncertainties is a challenging problem in …
Insights into the application of machine learning in reservoir engineering: current developments and future trends
H Wang, S Chen - Energies, 2023 - mdpi.com
In the past few decades, the machine learning (or data-driven) approach has been broadly
adopted as an alternative to scientific discovery, resulting in many opportunities and …
adopted as an alternative to scientific discovery, resulting in many opportunities and …
Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR
Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS)
by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS …
by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS …
Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models
Well-log interpretation estimates in situ rock properties along well trajectory, such as
porosity, water saturation, and permeability, to support reserve-volume estimation …
porosity, water saturation, and permeability, to support reserve-volume estimation …
Automatic semivariogram modeling by convolutional neural network
Modeling the semivariogram to characterize spatial continuity requires expert geostatistical
knowledge and domain expertise about the spatial phenomenon of interest. Moreover …
knowledge and domain expertise about the spatial phenomenon of interest. Moreover …
Application of machine learning algorithms in classification the flow units of the Kazhdumi reservoir in one of the oil fields in southwest of Iran
By determining the hydraulic flow units (HFUs) in the reservoir rock and examining the
distribution of porosity and permeability variables, it is possible to identify areas with suitable …
distribution of porosity and permeability variables, it is possible to identify areas with suitable …
Efficient subsurface modeling with sequential patch generative adversarial neural networks
Subsurface modeling is important for subsurface resource development, energy storage,
and CO2 sequestration. Many geostatistical and machine learning methods are developed …
and CO2 sequestration. Many geostatistical and machine learning methods are developed …