Summary
Accurate characterization of fracture geometry and production profiles is critical for optimizing completion designs in unconventional 
reservoirs.On 
average, the 6- cluster stages exhibit longer fracture half- lengths (median ~200 ft vs. ~160 ft), larger stimulated reservoir volume (SRV) 
per cluster (median ~2.5 ft vs. ~1.0 ft), and slightly higher fracture heights (median ~180 ft vs. ~150 ft) compared with the 10- cluster stag
es. While the median fracture conductivity remains similar between the two, greater heterogeneity is observed in the 10- cluster design.Furthermore, the findings offer valuable insights into the impact of different completion designs on fracture geometry and 
production distribution, contributing to the optimization of completion strategies and the accurate estimation of production performance 
using fiber- optic strain measurements in unconventional reservoirsRayleigh frequency shift (RFS) distributed strain sensing (DSS) is a powerful diagnostic tool for mapping fracture geometries 
and identifying production profiles along horizontal wells.Distinct differences exist between the 
two completion designs: The 10- cluster stages generally exhibit higher peak strain values (median ~32 u?) compared with the 6- cluster 
stages (median ~22 u?), while the 6- cluster stages present wider strain widths (median ~15 ft vs. ~11 ft).The proposed workflow reduces uncertainty, quantifies fracture geometries, and identifies production profiles, enabling robust 
completion optimization.This study pioneers the systematic analysis of strain responses measured along an entire horizontal producing well during the shut- in 
period in unconventional reservoirs, leveraging machine learning-based techniques.A machine learn
ing-based Markov Chain Monte Carlo (MCMC) workflow is developed for automated history matching of strain responses along the 
wellbore.Strain change attributes are assessed at the levels of completion 
designs, stages, and clusters.