Results 171 to 180 of about 29,542 (239)

Petrophysics

2021
Рассмотрены основные петрофизические свойства горных пород, слага-ощих разрезы месторождений: инженерные, физико-механические, электриче-ские, теплофизические и другие свойства горных пород, а также свойства минералов в ультрадисперсном состоянии. Подробно описаны петрофизические свойства наночастиц и наноматериалов.
V Zakryakin, V Portnov, M Ibatov, A Maussymbayeva, F Issatayeva, R Madisheva   +5 more
openaire   +2 more sources

Objective-Driven Solid-Surface-Roughness Characterization for Enhanced Nuclear-Magnetic-Resonance Petrophysics

SPE Journal, 2021
Typically, smooth solid surfaces of reservoir rocks are assumed in formation evaluation, such as nuclear-magnetic-resonance (NMR) petrophysics and reservoir-wettability characterization through contact-angle measurements.
S. Ma, G. Singer, Songhua Chen, M. Eid
semanticscholar   +1 more source

3D Printing for Experiments in Petrophysics, Rock Physics, and Rock Mechanics: A Review

SPE Reservoir Evaluation and Engineering, 2021
Geoscientific and engineering experiments in petrophysics, rock physics, and rock mechanics depend on multiple, costly, and sometimes rare samples used to characterize the properties of natural rocks.
L. Kong, S. Ishutov, F. Hasiuk, Chicheng Xu   +3 more
semanticscholar   +1 more source

Tutorial: Petrophysics of Thinly Bedded Formations

Petrophysics, 2021
This paper was written in response to a personal request from the Editor to contribute a “tutorial” on thin-bed petrophysics. The paper is not intended to be an exhaustive review of all the work that has been done regarding thin-bed petrophysics. Instead,
R. Aldred
semanticscholar   +1 more source

Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multi-trace Bayesian AVO petrophysics inversion in multi-porosity reservoirs

Geophysics, 2021
The estimation of petrophysical and fluid-filling properties of subsurface reservoirs from seismic data is a crucial component of reservoir characterization.
Kun Li, Xingyao Yin, Z. Zong, D. Grana
semanticscholar   +1 more source

A taphofacies interpretation of shell concentrations and their relationship with petrophysics: A case study of Barremian-Aptian coquinas in the Itapema Formation, Santos Basin-Brazil

, 2020
The Itapema Formation in the Santos Basin is described as a carbonate coquina interval, a type of sedimentary rock composed mainly by shells and their fragments.
G. Chinelatto, A. M. P. Belila, M. Basso, J. Souza, A. Vidal   +4 more
semanticscholar   +1 more source

PETROPHYSICS SKILL SET GUIDELINES FOR INDEPENDENT PETROPHYSICAL CONTRIBUTORS

SPWLA 62nd Annual Online Symposium Transactions, 2021
During the 2014 SPWLA Topical Meeting on “Educating the Petrophysicist”, it recommended that “A minimum set of standards in terms of both knowledge and skills (competencies) for an entry level petrophysicist (SPWLA, 2014). Similar proposal has been raised before as well (Loermans, 2002). With the rapid advance in technology, continuous crew change, and
Z. Zack Liu, S. Mark Ma
openaire   +1 more source

Evaluation of coal petrophysics incorporating fractal characteristics by mercury intrusion porosimetry and low-field NMR

, 2020
Mercury intrusion porosimetry (MIP) and low-field nuclear magnetic resonance (NMR) were combined to investigate pore-fracture structure and fractal characteristics of coals (0.93%  k N = 0.0045 ∗ e ϕ NM 0.6851 + 0.037 ), ranging from 0.043 to 2.767 mD ...
Zhentao Li, Dameng Liu, Yidong Cai, Yunpeng Wang, Guangyao Si   +4 more
semanticscholar   +1 more source

Quantitative pore‐type characterization from well logs based on the seismic petrophysics in a carbonate reservoir

Geophysical Prospecting, 2020
Quality, availability and consistency of the measured and interpreted well log data are essential in the seismic reservoir characterization methods, and seismic petrophysics is the recommended workflow to achieve data consistency between logs and seismic
M. Mirkamali, A. Javaherian, H. Hassani, M. Saberi, S. A. Hosseini   +4 more
semanticscholar   +1 more source

A Practical Guide to Machine Learning in Petrophysics: Navigating the Chaos

Middle East Oil, Gas and Geosciences Show (MEOS GEO)
The integration of machine-learning (ML) into petrophysical workflows has transformed formation evaluation and reservoir characterization. However, the overwhelming variety of ML approaches (algorithms/ workflows) and data challenges often hinder ...
S. Sarkar, S. Dasgupta, A. C. Biundo, O. Berkimbay   +3 more
semanticscholar   +1 more source