Open Access

ARTICLE

A Temperature-Pressure Coupled Model for Predicting Sand Production during Multi-Thermal Fluid Huff-and-Puff in Unconsolidated Sandstone Reservoirs

Zhuwei Tao, Yanfeng He^*, Hui Xu^*, Shengda Zhang, Zetao Sun, Ying Wu, Peng Li, Xing Shi

School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou, China

* Corresponding Authors: Yanfeng He. Email: ; Hui Xu. Email:

Fluid Dynamics & Materials Processing 2026, 22(8), 9 https://doi.org/10.32604/fdmp.2026.086018

Received 22 May 2026; Accepted 01 September 2026; Issue published 04 September 2026

Abstract

This study elucidates the mechanisms governing sand production during multi-thermal fluid huff-and-puff in unconsolidated sandstone heavy oil reservoirs and develops a temperature-pressure coupled prediction model for accurately quantifying sand production. Orthogonal laboratory experiments were conducted on reservoir samples from a representative case (Block X, Oilfield L), to compare the mechanical response and sand production behavior under multi-thermal fluid and conventional steam stimulation. The relative importance of the governing parameters was quantified using analysis of variance (ANOVA), and an exponential prediction model incorporating the coupled effects of temperature and pressure was established. The results reveal that temperature and pressure are the primary factors controlling both rock stiffness degradation and sand production, with highly significant statistical effects. Compared with conventional steam injection, multi-thermal fluid stimulation induces more pronounced rock weakening and substantially greater sand production owing to the combined effects of CO₂ dissolution and N₂ gas channelling. It also generates coarser produced particles and promotes wormhole formation. The optimum operating conditions were identified as an injection temperature of 130°C, a pressure of 10 MPa, a CO₂/N₂ volume ratio of 0.5, and an injection volume of 10 mL. The proposed model reproduces the experimental observations with excellent accuracy, achieving a coefficient of determination exceeding 0.98 and consistently outperforming conventional single-variable prediction models.

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Keywords

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