Hematpur Hamed, Hosseini Seyednooroldin, Mahmood Syed Mohammad, Abdollahi Reza, Hamdi Zakaria, Ghamarpoor Reza
EOR department, Research Institute of Petroleum Industry, Tehran, Iran.
Petroleum Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
Sci Rep. 2025 Jul 2;15(1):22829. doi: 10.1038/s41598-025-05847-8.
Dry-Out is one of the most critical phenomena in foam flooding, especially in Surfactant-Alternating-Gas. Commercial reservoir simulators are equipped with different Dry-Out models which have two main issues; first, even for the water saturation less than the limiting value, the current models are not able to dry out the foam thoroughly, second, the data from steady-state foam flooding experiments are required to estimate the model parameters. This study aims to address these issues in two separate parts. Firstly, it proposes a modified Dry-Out function to tackle the inadequacies of models in the vicinity of limiting water saturation without any discontinuity. It employs the error function to reveal the foam coalescence and cause the singularity in the current models to disappear. This model is verified with foam flooding experimental data. Secondly, the Genetic Algorithm and Nonlinear Least Square are used to develop the new approaches to estimate the Dry-Out parameters using the steady and unsteady state foam flooding experimental data, respectively. The set of steady and unsteady state foam flooding is conducted to validate the proposed approach for the model's parameters estimation. Eventually, foam flooding is numerically simulated to verify the modified model and parameters estimation approaches. The modified model indicates an acceptable fit with observed data compared with commercial simulators. The novel methodology for obtaining fitting parameters from unsteady state flooding yields precise parameters, thereby obviating the need for time-intensive steady state flooding experiments.
干涸是泡沫驱油中最关键的现象之一，尤其是在表面活性剂交替气体驱油过程中。商业油藏模拟器配备了不同的干涸模型，但存在两个主要问题：第一，即使水饱和度低于极限值，当前模型也无法彻底使泡沫干涸；第二，需要稳态泡沫驱油实验的数据来估计模型参数。本研究旨在分两个部分解决这些问题。首先，提出一种改进的干涸函数，以解决模型在极限水饱和度附近的不足之处，且无任何不连续性。它采用误差函数来揭示泡沫聚并情况，并使当前模型中的奇异性消失。该模型通过泡沫驱油实验数据进行了验证。其次，分别使用遗传算法和非线性最小二乘法，开发了利用稳态和非稳态泡沫驱油实验数据来估计干涸参数的新方法。进行了稳态和非稳态泡沫驱油实验集，以验证所提出的模型参数估计方法。最终，对泡沫驱油进行了数值模拟，以验证改进后的模型和参数估计方法。与商业模拟器相比，改进后的模型与观测数据显示出可接受的拟合度。从非稳态驱油中获取拟合参数的新方法产生了精确的参数，从而无需进行耗时的稳态驱油实验。
