Morphology-Aware Experimental Evaluation of Proppant-Supported Fracture Conductivity and Surface Roughness Evolution in Tight Sandstone Fractures

Particle-laden flow through rough confined fractures is controlled by the coupled evolution of particle packing, load-bearing contacts, and rough-wall flow channels. In this study, conductivity experiments were performed on rough split-core fractures prepared from downhole tight-sandstone cores from the Tarim Basin, China, to examine how proppant size mixing and placement sequence regulate flow capacity under closure. Single-size 40/70 and 70/140 proppants and mixed-size systems with different size ratios were tested under staged and uniformly mixed placement schemes. Two equivalent placement levels, denoted as 1 mm and 2 mm, were considered. Three-dimensional laser scanning before and after conductivity testing was used to quantify rough-wall morphology using Ra, Rq, and Rz. The results show that fracture conductivity decreased with increasing closure pressure for all particle systems, indicating progressive narrowing and rearrangement of preferential flow channels. Coarse-particle-dominated systems consistently retained higher conductivity, with an overall ranking of 40/70 > 3:1 > 1:1 > 1:3 > 70/140 at both placement levels. Increasing the placement level from 1 mm to 2 mm markedly enhanced conductivity, especially for systems rich in 40/70 proppant. Staged placement yielded higher conductivity than uniformly mixed placement for the 3:1 and 1:1 systems, but this effect was negligible for the fine-particle-dominated 1:3 system. Post-test roughness changes indicate that sparse placement induced competing smoothing and roughening, whereas sufficient placement caused systematic roughening after closure. The proposed morphology-aware experimental workflow provides a laboratory-scale basis for interpreting the coupled evolution of fracture conductivity and rough-wall morphology in propped rough fractures. Although the workflow can be extended to other lithologies and fracture systems, quantitative field-scale prediction requires further calibration with larger datasets and reservoir-specific conditions.