Pore pressure prediction by empirical and machine learning methods using conventional and drilling logs in carbonate rocks
MR Delavar, A Ramezanzadeh - Rock Mechanics and Rock Engineering, 2023 - Springer
Precise pore pressure estimation has high significance in terms of drilling and development
operations. Regarding its necessity, empirical and intelligence methods have been …
operations. Regarding its necessity, empirical and intelligence methods have been …
Strength prediction and application of cemented paste backfill based on machine learning and strength correction
B Zhang, K Li, S Zhang, Y Hu, B Han - Heliyon, 2022 - cell.com
Cemented paste backfill (CPB) is wildly used in mines production practices around the
world. The strength of CPB is the core of research which is affected by factors such as slurry …
world. The strength of CPB is the core of research which is affected by factors such as slurry …
A deep learning-based multi-fidelity optimization method for the design of acoustic metasurface
A desirable acoustic metasurface requires the scattered acoustic field distribution uniform.
Neural networks are effective substitutions to mimic the expensive FE simulations in most …
Neural networks are effective substitutions to mimic the expensive FE simulations in most …
Pore-scale modeling of multiphase flow in porous media using a conditional generative adversarial network (cGAN)
Multiphase flow in porous media is involved in various natural and industrial applications,
including water infiltration into soils, carbon geosequestration, and underground hydrogen …
including water infiltration into soils, carbon geosequestration, and underground hydrogen …
Parametric investigation of particle swarm optimization to improve the performance of the adaptive neuro-fuzzy inference system in determining the buckling capacity …
In this paper, the main objectives are to investigate and select the most suitable parameters
used in particle swarm optimization (PSO), namely the number of rules (nrule), population …
used in particle swarm optimization (PSO), namely the number of rules (nrule), population …
[HTML][HTML] Application of A∗ algorithm for microstructure and transport properties characterization from 3D rock images
Thorough comprehension of flow behavior in underground porous media is fundamental for
several applications such as oil and gas production, Underground Gas Storage, CO2 …
several applications such as oil and gas production, Underground Gas Storage, CO2 …
Upscaling permeability anisotropy in digital sandstones using convolutional neural networks
Pore-scale modelling and implementation of micro x-ray Computed Tomography (μxCT)
images have become a reliable method to predict the petrophysical properties of rocks …
images have become a reliable method to predict the petrophysical properties of rocks …
Machine learning methods for estimating permeability of a reservoir
H Khan, A Srivastav, A Kumar Mishra… - International Journal of …, 2022 - Springer
The prediction of permeability from the information of a well log is a crucial and extensive
task that is observed in the earth sciences. The permeability of a reservoir is greatly …
task that is observed in the earth sciences. The permeability of a reservoir is greatly …
Evaluating machine learning techniques for carbonate formation permeability prediction using well log data
Machine learning has a significant advantage for many difficulties in the oil and gas industry,
especially when it comes to resolving complex challenges in reservoir characterization …
especially when it comes to resolving complex challenges in reservoir characterization …
Parameter identification of minifrac numerical tests using a gradient boosting‐based proxy model and genetic algorithm
In recent years, the petroleum industry has devoted considerable attention to studying fluid
flow inside fracture channels due to the discovery of naturally fractured reservoirs. The …
flow inside fracture channels due to the discovery of naturally fractured reservoirs. The …