Automatic Optimization of Pulse Sequences Based on a Closed-Loop Control Strategy

Abstract

Generally, the pulse sequence parameters and acquisition parameters of nuclear magnetic resonance (NMR) logging tools are determined before logging and kept unchanged during logging. Because the detection area changes constantly during logging, the preset parameters are often not the best for different detection objectives, the energy consumption and sampling resolution will be reduced. To solve this problem, we propose a closed-loop control scheme for parameter optimization, which achieves the dynamic regulation of parameters according to the relaxation characteristics of the samples. The closed-loop control system has been implemented in a laboratory core analyzer to prove the effectiveness of the variable TE sequence as a reconnaissance sequence. When the sample changes, the control system can guide the control circuit to switch to the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence with appropriate parameters in a timely manner. Compared with conventional parameter setting methods, this scheme can better avoid insufficient attenuation of the echo train or excessive data collection caused by the improper setting of pulse train length while reducing energy consumption during measurements.