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Expected-value techniques for Monte Carlo modeling of well logging problems.

Scott W Mosher1, Marko Maučec1, Jerome Spanier1

  • 1Claremont Research Institute of Applied Mathematical Sciences, School of Mathematics, Claremont Graduate University, Claremont, CA 91711, USA.

Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces expected-value (EV) estimation to enhance Monte Carlo simulations for oil well logging. EV estimators improve simulation efficiency and information content by utilizing particle data more effectively.

Keywords:
Expected-value estimatorMonte Carlo simulationsOil-well logging

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Area of Science:

  • Nuclear Engineering
  • Computational Physics
  • Petroleum Engineering

Background:

  • Monte Carlo simulations are crucial for modeling particle transport in oil well logging.
  • Conventional estimators in these simulations have limitations in extracting information from incomplete particle histories.
  • Improving the efficiency and accuracy of these simulations is vital for resource exploration and characterization.

Purpose of the Study:

  • To develop and describe expected-value (EV) estimation techniques for Monte Carlo simulations in oil well logging.
  • To enhance the efficiency and information content of these simulations.
  • To provide a method for accurately estimating detector responses like flux, reaction rates, and pulse-height spectra.

Main Methods:

  • Development of two novel expected-value (EV) estimators tailored for oil well logging scenarios.
  • Application of EV estimation to average interaction and transport probabilities exactly at the event level.
  • Integration of EV estimators with existing variance reduction techniques, such as weight-window methods.

Main Results:

  • The developed EV estimators successfully extract information from particles that do not reach the detector.
  • The first EV estimator accurately calculates volume-averaged scalar flux and reaction rates.
  • The second EV estimator provides a weighted surface-averaged incident current for estimating pulse-height spectra.

Conclusions:

  • Expected-value estimation significantly improves the information content and efficiency of Monte Carlo simulations for oil well logging.
  • EV estimators provide an efficient method for obtaining crucial detector data, including fluxes, reaction rates, and pulse-height spectra.
  • The combination of EV estimation and weight-window variance reduction techniques offers a powerful approach for advanced well logging simulations.