EN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
- 油气藏评价与开发 2026年16卷第2期 页码：414-422
- 作者机构：
1.西南石油大学石油与天然气工程学院，四川 成都 610500
2.陕西省二氧化碳封存与提高采收率重点实验室，陕西 西安 710065
3.中国石油勘探开发研究院，北京 100083
4.中国石油长庆油田分公司油气工艺研究院，陕西 西安 710018
- 作者简介：
冯国庆（1974—），男，博士，教授，主要从事油气藏数值模拟、油气藏工程、储层建模、测井资料解释等方向的科研和教学工作。地址：四川省成都市新都区新都大道8号，邮政编码：610500。E-mail： drfenggq@163.com
- 基金信息：陕西省二氧化碳封存与提高采收率重点实验室开放课题“特低渗油藏改善性水驱与二氧化碳微分散体系驱技术数值模拟方法研究”(YJSYZX25SKF0008)
DOI：10.13809/j.cnki.cn32-1825/te.2024601
中图分类号： TE348收稿：2025-04-21，
纸质出版：2026-03-26
- 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.
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.
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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