基于ANN算法的微纳米水气分散体系驱产油量预测方法

摘要/Abstract

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。

关键词: 微纳米水气分散体系驱, 机器学习, K-Means聚类分析, 人工神经网络, 莱文贝格-马夸特算法

Abstract: The Micro-nano water-gas dispersion system drive (MNWDS) is a new type of low permeability reservoir technology to improve the recovery rate of low permeability reservoirs, which improves the mobility of the drive system and the efficiency of the drive through the injection of micro-nanometer scale gas and water dispersed phases, and it is especially suitable for low permeability reservoirs where the effect of the conventional water drive is poor, and the mine experiments have been carried out one after another. At present, this method has been carried out in the experimental area of Wuliwan Chang6. When using numerical simulation methods to predict the oil yield driven by the micro-nano water-air dispersion system, it is necessary to consider multiple parameters such as bubble size, gas-liquid ratio, fluid properties and complex gas-liquid interactions. The process is complex and time-consuming, and it is impossible to quickly simulate the oil production driven by the micro-nano water-gas dispersion system. In order to accurately predict the production of oil wells after injecting the micro-nano water-gas dispersion system, this paper establishes a production prediction model of micro-nano water-gas dispersion system based on the actual production data of the test area and the parameters of the geological model, and utilizes the artificial neural network (ANN) algorithm. The model takes the oil production data of the wells in the test area, water content, injection volume, water-driven reserves, reservoir thickness, permeability and porosity as input parameters, and the oil production 12 months after implementation as output, and establishes the training sample set of the model. Through K-Means clustering analysis of the sample set, invalid samples were eliminated, and a training set of 59 samples was finally formed. In the model training, an optimization algorithm was introduced to automatically adjust the model parameters, which significantly improved the test set prediction accuracy of the model. Based on this model, the oil production prediction was carried out for 21 well groups to be driven by the micro-nano water-gas dispersion system, and the comparison between the prediction results and with the numerical simulation results showed that the compliance rate of the two was as high as 95%, which verified the accuracy of the current model. The model provides a new way for the oil production prediction of micro-nano water-gas dispersion system drive.

Key words: Micro-nano-water-gas dispersion system flooding, machine learning, K-Means clustering analysis, artificial neural network, Levenberg-Maquardt

中图分类号:

TE348

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, 0, (): 2024601-2024601.

FENG GUOQING,CHANG HAILING,WANG KEYU, et al. Prediction method of oil production driven by micro-nano water and gas dispersion system based on ANN algorithm[J]. Petroleum Reservoir Evaluation and Development, 0, (): 2024601-2024601.

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