[HTML][HTML] Predicting permeability from 3D rock images based on CNN with physical information

P Tang, D Zhang, H Li - Journal of Hydrology, 2022 - Elsevier
Permeability is one of the most important properties in subsurface flow problems, which
measures the ability of rocks to transmit fluid. Normally, permeability is determined through …

[HTML][HTML] Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review

F Anifowose, M Mezghani, S Badawood… - Applied Computing and …, 2022 - Elsevier
The current utility of mud gas data is typically limited to geological and petrophysical
correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the …

Deep learning and time-series analysis for the early detection of lost circulation incidents during drilling operations

M Aljubran, J Ramasamy, M Albassam… - IEEE …, 2021 - ieeexplore.ieee.org
Drilling operations consist of breaking the rock to deepen a wellbore for oil or gas extraction.
A drilling fluid, circulating from the surface through the drill pipe and from the annulus to the …

Well control space out: A deep-learning approach for the optimization of drilling safety operations

A Magana-Mora, M Affleck, M Ibrahim… - IEEE …, 2021 - ieeexplore.ieee.org
As drilling of new oil and gas wells increase to meet energy demands, it is essential to
optimize processes to ensure the health and safety of the crew as well as the protection of …

Quantitative assessment of rock lithology from gamma-ray and mud logging data

A Shakirov, A Molchanov, L Ismailova… - Geoenergy Science and …, 2023 - Elsevier
The data on vertical variations of the rock lithological content along the geological profiles of
boreholes are essential for interwell correlation, geological or hydrodynamic modeling …

Hybrid physics-field data approach improves prediction of ROP/drilling performance of sharp and worn PDC bits

GD Zhan, A Magana-Mora, E Moellendick… - International …, 2021 - onepetro.org
This study presents a hybrid approach that combines data-driven and physics models for
worn and sharp drilling simulation of polycrystalline diamond compact (PDC) bit designs …

A borehole porosity prediction method with focusing on local shape

J Li, T Xu, W Zhang, H Liu, Y Kang, W Lv - Geoenergy Science and …, 2023 - Elsevier
Porosity is a valuable parameter reflecting petroleum storage performance and plays an
important role in reservoir exploration. Various studies have confirmed the feasibility of …

Projecting Petrophysical Logs at the Bit through Multi-Well Data Analysis with Machine Learning

A Sharma, T Burak, R Nygaard, E Hoel… - SPE Offshore Europe …, 2023 - onepetro.org
The vertical distance from logging while drilling (LWD) sensors to the bit is often more than
30m (98 ft), which leads to difficulty in performing real-time comparison of LWD and drilling …

Fluid identification with Graph Transformer using well logging data

Y Sun, S Pang, Y Zhang - Physics of Fluids, 2024 - pubs.aip.org
The prediction of fluid through well logging is a cornerstone in guiding exploratory efforts in
the energy sector. Comprehending the fluid composition beneath the surface empowers …

Advancing fluid identification via well-logging data: Leveraging persistent initialization and transformer modeling

Y Sun, S Pang, Y Zhang - Physics of Fluids, 2024 - pubs.aip.org
In the domain of energy exploration, the forecasting of fluid via well logging is pivotal in
directing exploration endeavors. Understanding the composition of fluid underground is key …