Yang Yu^1,2, Sheng Fan^1,2, Zhonglin Li^1,2, Zhong He^1,2, Jingwei Liu^3,*, Peng Xu^3,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.8, pp. 1875-1894, 2025, DOI:10.32604/fdmp.2025.066404 - 12 September 2025

Abstract Drilling operations in carbonate rock heavy oil blocks (e.g., in the Tahe Oilfield) are challenged by the intrusion of high-viscosity, temperature-sensitive formation heavy oil into the drilling fluid. This phenomenon often results in wellbore blockage, reduced penetration rates, and compromised well control, thereby significantly limiting drilling efficiency and operational safety. To address this issue, this study conducts a comprehensive investigation into the mechanisms governing heavy oil invasion using a combination of laboratory experiments and field data analysis. Findings indicate that the reservoir exhibits strong heterogeneity and that the heavy oil possesses distinctive physical properties. The… More >

Nannan Liu^*, Hengchen Qi, Hui Xu, Yanfeng He

FDMP-Fluid Dynamics & Materials Processing, Vol.20, No.8, pp. 1731-1741, 2024, DOI:10.32604/fdmp.2024.048354 - 06 August 2024

Abstract Low salinity water containing sulfate ions can significantly alter the surface wettability of carbonate rocks. Nevertheless, the impact of sulfate concentration on the desorption of oil film on the surface of carbonate rock is still unknown. This study examines the variations in the wettability of the surface of carbonate rocks in solutions containing varying amounts of sodium sulfate and pure water. The problem is addressed in the framework of molecular dynamics simulation (Material Studio software) and experiments. The experiment’s findings demonstrate that sodium sulfate can increase the rate at which oil moisture is turned into More > Graphic Abstract

Xianhe Yue^*, Shunshe Luo

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.4, pp. 1195-1203, 2022, DOI:10.32604/fdmp.2022.020649 - 06 April 2022

Abstract Because carbonate rocks have a wide range of reservoir forms, a low matrix permeability, and a complicated seam hole formation, using traditional capacity prediction methods to estimate carbonate reservoirs can lead to significant errors. We propose a machine learning-based capacity prediction method for carbonate rocks by analyzing the degree of correlation between various factors and three machine learning models: support vector machine, BP neural network, and elastic network. The error rate for these three models are 10%, 16%, and 33%, respectively (according to the analysis of 40 training wells and 10 test wells). More >