Simulation of Borehole Nuclear Measurements: A Practical Tutorial Guide for Implementation of Monte Carlo Methods and Approximations Based on Flux Sensitivity Functions

Luycx, Mathilde; Bennis, Mohamed; Torres‐Verdín, Carlos; Preeg, William E.

doi:10.30632/pjv61n1-2020t1

Cited by 7 publications

(2 citation statements)

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“…Key parameters such as source-detectors distances, detectors and shield volumes and tool dimensions are close to real logging tools. Generic logging tools models were developed by the Austin University [14] [15].…”

Section: Geometry and Visualizationmentioning

confidence: 99%

Comparison between GEANT4 and MCNP for well logging applications

Varignier,

Fondement,

Carasco

et al. 2023

EPJ Web Conf.

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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.

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Section: Geometry and Visualizationmentioning

confidence: 99%

Comparison between GEANT4 and MCNP for well logging applications

Varignier,

Fondement,

Carasco

et al. 2023

EPJ Web Conf.

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show abstract

“…The density simulations are conducted using the forward simulator developed by [Mendoza et al, 2010, ] which relies on flux sensitivity functions. A detailed explanation of the method can be found in the work by [Luycx et al, 2020, ].…”

Section: Density Modelmentioning

confidence: 99%

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

Jahani¹,

Garrido²,

Alyaev³

et al. 2021

Preprint

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The costs for drilling offshore wells are high and hydrocarbons are often located in complex reservoir formations. To effectively produce from such reservoirs and reduce costs, optimized well placement in real-time (geosteering) is crucial. Geosteering is usually assisted by an updated formation evaluation obtained by well-log interpretation while drilling. A reliable, computationally efficient, and robust workflow to interpret well logs and capture uncertainties in real-time is necessary for this application. An iterative ensemble-based method, namely the approximate Levenberg Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML) is integrated in our formation evaluation workflow. We estimate model parameters, resistivity and density in addition to boundary locations, and related uncertainties by reducing the statistical misfit between the measurements from the well logging tools and the theoretical measurements from the forward tool simulators. The results of analyzing several synthetic cases with several types of logs verified that the proposed method can give good estimate of model parameters by employing as few as 40 ensemble members and 2-10 iterations. By comparing the CPU time, we conclude that the proposed method has at least about 10-125 times lower computational time compare to a common statistical method, such as Metropolis-Hastings Monte Carlo. In addition, the ensemble-based method can run in parallel on multiple CPUs. The reliability and speed of well-log interpretation is normally sensitive to several parameters such as the distances between the formation boundaries and the logging tool, model parameter's contrast, formation layer thickness and well inclination. Testing the method on a case inspired from a real field also yielded accurate formation evaluation. Thus, the proposed ensemble-based method has been proven robust and computationally efficient to estimate petrophysical formation properties, layer boundaries and their uncertainties, indicating that it is suitable for geosteering.

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A fast forward computational method for nuclear measurement using volumetric detection constraints

Zhang,

Lin

2024

NUCL SCI TECH

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Simulation of Borehole Nuclear Measurements: A Practical Tutorial Guide for Implementation of Monte Carlo Methods and Approximations Based on Flux Sensitivity Functions

Cited by 7 publications

References 0 publications

Comparison between GEANT4 and MCNP for well logging applications

Comparison between GEANT4 and MCNP for well logging applications

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

A fast forward computational method for nuclear measurement using volumetric detection constraints

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