Liang Wang¹, Baofeng Hou¹, Yanming Fang³, Jintao Zhang², Fajian Nie^1,2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.7, pp. 1925-1937, 2023, DOI:10.32604/fdmp.2023.025966

Abstract Oxygen-reducing air flooding is a low-permeability reservoir recovery technology with safety and low-cost advantages. However, in the process of air injection and drive, carbon in the air is oxidized through the crude oil reservoir to generate CO₂, and this can cause serious corrosion in the recovery well. In this study, experiments on the corrosion of J55 tubular steel in a fluid environment with coexisting O₂ and CO₂ in an autoclave are presented. In particular, a weight loss method and a 3D morphometer were used to determine the average and the local corrosion rate. The corrosion surface morphology and composition were… More >

Xuanyu Dong^1,*, Jingyao Yang²

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.1, pp. 175-185, 2023, DOI:10.32604/fdmp.2022.021345

Abstract A new numerical model for low-permeability reservoirs is developed. The model incorporates the nonlinear characteristics of oil-water two-phase flows while taking into account the initiation pressure gradient. Related numerical solutions are obtained using a finite difference method. The correctness of the method is demonstrated using a two-dimensional inhomogeneous low permeability example. Then, the differences in the cumulative oil and water production are investigated for different starting water saturations. It is shown that when the initial water saturation grows, the water content of the block continues to rise and the cumulative oil production gradually decreases. More >

Guowei Zhu^*, Kangliang Guo, Haoran Yang, Xinchen Gao, Shuangshuang Zhang

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.5, pp. 1521-1528, 2022, DOI:10.32604/fdmp.2022.020942

Abstract In order to overcome the typical limitations of numerical simulation methods used to estimate the production of low-permeability reservoirs, in this study, a new data-driven approach is proposed for the case of water-driven hypo-permeable reservoirs. In particular, given the bottlenecks of traditional recurrent neural networks in handling time series data, a neural network with long and short-term memory is used for such a purpose. This method can reduce the time required to solve a large number of partial differential equations. As such, it can therefore significantly improve the efficiency in predicting the needed production performances. Practical examples about water-driven hypotonic… More >