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Comparison between GEANT4 and MCNP for well logging applications.

Authors :
Varignier, Geoffrey
Fondement, Valentin
Carasco, Cédric
Collot, Johann
Pérot, Bertrand
Marchais, Thomas
Chuilon, Pierre
Caroli, Emmanuel
Doan, Mai-Linh
Source :
EPJ Web of Conferences. 11/21/2023, Vol. 288, p1-8. 8p.
Publication Year :
2023

Abstract

MCNP and GEANT4 are two reference Monte Carlo nuclear simulators, MCNP being the standard in the Oil & Gas nuclear logging industry. While performing a simulation benchmark of these two software for the purpose of "Cased Hole" wellbore evaluation, discrepancies between MCNP and GEANT4 were observed: computational experiments were performed first in a theoretical and simplified environment using spherical models, then in a more realistic "Open Hole" wellbore context with simplified logging tools. Results of this comparison show an excellent overall agreement for gamma-gamma physics and an acceptable agreement for neutron-neutron physics. However, the agreement for neutron-gamma physics is satisfactory only for certain lithologies and energy windows, but not acceptable for other operating conditions. These results need to be put in perspective with the current use of nuclear simulation in the logging industry. Indeed, wellbore evaluations rely on charts simulated with Monte Carlo codes in various contexts. In the case of radially heterogeneous environments such as "Cased Hole" wellbores, nuclear simulations are mandatory to precisely determine the radial sensitivity of logging tools via the so-called sensitivity functions. The feasibility of wellbore inversion relies on the physical validity of such sensitivity functions obtained from nuclear simulations. This MCNP vs. GEANT4 benchmark was conducted with the perspective to secure the physical fundamentals used for building the sensitivity functions of logging tools. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21016275
Volume :
288
Database :
Academic Search Index
Journal :
EPJ Web of Conferences
Publication Type :
Conference
Accession number :
173805174
Full Text :
https://doi.org/10.1051/epjconf/202328801002