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

• Published in: Nuclear science and techniques Vol. 35; no. 2
• Main Authors: Zhang, Qiong, Lin, Lin-Lv
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.02.2024
• Subjects: Beam Physics; Nuclear Energy; Particle Acceleration and Detection; Particle and Nuclear Physics; Physics; Physics and Astronomy; Nuclear measurement; Fast forward computation; Volumetric constraints
• ISSN: 1001-8042; 2210-3147
• DOI: 10.1007/s41365-024-01393-6

Owing to the complex lithology of unconventional reservoirs, field interpreters usually need to provide a basis for interpretation using logging simulation models. Among the various detection tools that use nuclear sources, the detector response can reflect various types of information of the medium. The Monte Carlo method is one of the primary methods used to obtain nuclear detection responses in complex environments. However, this requires a computational process with extensive random sampling, consumes considerable resources, and does not provide real-time response results. Therefore, a novel fast forward computational method (FFCM) for nuclear measurement that uses volumetric detection constraints to rapidly calculate the detector response in various complex environments is proposed. First, the data library required for the FFCM is built by collecting the detection volume, detector counts, and flux sensitivity functions through a Monte Carlo simulation. Then, based on perturbation theory and the Rytov approximation, a model for the detector response is derived using the flux sensitivity function method and a one-group diffusion model. The environmental perturbation is constrained to optimize the model according to the tool structure and the impact of the formation and borehole within the effective detection volume. Finally, the method is applied to a neutron porosity tool for verification. In various complex simulation environments, the maximum relative error between the calculated porosity results of Monte Carlo and FFCM was 6.80%, with a root-mean-square error of 0.62 p.u. In field well applications, the formation porosity model obtained using FFCM was in good agreement with the model obtained by interpreters, which demonstrates the validity and accuracy of the proposed method.