基于ANN算法的微纳米水气分散体系驱产油量预测方法

冯国庆; 常海铃; 王苛宇; 吴琳; 伍家忠; 王石头

doi:10.13809/j.cnki.cn32-1825/te.2024601

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基于ANN算法的微纳米水气分散体系驱产油量预测方法
An oil production prediction method for micro-nano water-gas dispersion system flooding based on ANN algorithm
2026年16卷第2期页码：414-422
收稿：2025-04-21，

纸质出版：2026-03-26
DOI： 10.13809/j.cnki.cn32-1825/te.2024601
稿件说明：

移动端阅览

冯国庆, 常海铃, 王苛宇, 等. 基于ANN算法的微纳米水气分散体系驱产油量预测方法[J]. 油气藏评价与开发, 2026, 16(2): 414-422. DOI： 10.13809/j.cnki.cn32-1825/te.2024601.

FENG Guoqing, CHANG Hailing, WANG Keyu, et al. An oil production prediction method for micro-nano water-gas dispersion system flooding based on ANN algorithm[J]. Petroleum Reservoir Evaluation and Development, 2026, 16(2): 414-422. DOI： 10.13809/j.cnki.cn32-1825/te.2024601.

摘要

微纳米水气分散体系驱（MNWDS）是1种新型的提高采收率技术，通过微纳米尺度的气水分散相注入，能够进入更小的孔隙空间，从而扩大了波及体积，有效提高了采收率。目前，该方法已在五里湾长6试验区开展矿场实验。在采用数值模拟方法预测微纳米水气分散体系驱的产油量时，需要考虑气泡尺寸、气液比、流体性质等多参数及复杂的气液相互作用，过程复杂且耗时长，无法快速模拟微纳米水气分散体系驱的产油量。为能够准确地预测注入微纳米水气分散体系驱后油井的产油量，该研究基于试验区实际生产数据和地质模型参数，运用人工神经网络（ANN）算法，建立了微纳米水气分散体系驱的产油量预测模型。该模型以试验区微纳米水气分散体系实施前油井的产油量、含水率、渗透率、注入微纳米水气分散体系量、水驱储量、孔隙度、有效厚度作为输入参数，以实施后12个月的产油量作为输出参数，建立了模型的训练样本集。通过对样本集进行K-Means（K-均值聚类算法）聚类分析，剔除了无效样本，最终形成了59个样本的训练集。在模型训练中，引入优化算法自动调整模型参数，显著提高了模型的测试集预测精度。基于此模型，对即将实施微纳米水气分散体系驱的21个井组进行了产油量预测，预测结果与数值模拟结果对比表明，二者的符合率高达95%，验证了该次模型的准确性。该模型为微纳米水气分散体系驱的产油量预测提供了1个新的途径。

Abstract

Micro-nano water-gas dispersion system (MNWDS) flooding is a novel enhanced oil recovery technique. By injecting gas-water dispersed phases at the micro–nano scale

it can enter smaller pore spaces

thereby expanding the swept volume and effectively improving recovery efficiency. At present

field tests of this method have been conducted in the Wuliwan Chang 6 pilot area. When using numerical simulation to predict oil production after MNWDS flooding

multiple parameters such as bubble size

gas-liquid ratio

and fluid properties

as well as complex gas-liquid interactions

must be considered. This process is complex and time-consuming

making it difficult to rapidly simulate oil production after MNWDS injection. This study aims to accurately predict the oil production of wells after MNWDS injection. Based on actual production data from the test area and geological model parameters

an artificial neural network (ANN) algorithm was employed to establish a production prediction model for MNWDS flooding. The model used oil production

water cut

permeability

injected MNWDS volume

waterflooding reserves

porosity

and effective thickness of wells before MNWDS injection in the test area as input parameters

and used the oil production over the 12 months after flooding as the output parameter to construct the training sample set. K-means clustering analysis was performed on the sample set to remove invalid samples

and a training set of 59 samples was finally obtained. During model training

an optimization algorithm was introduced to automatically adjust model parameters

which significantly improved prediction accuracy on the test set. Based on this model

oil production was predicted for 21 well groups scheduled to undergo MNWDS flooding. Comparisons with numerical simulation results indicated an agreement rate of up to 95%

verifying the accuracy of this model. The model provides a new approach for oil production prediction of MNWDS flooding.

关键词

Keywords

references

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