针对传统时间序列模型无法对短生产周期天然气井进行产气量预测的问题,首先采用机器学习的近邻传播算法对天然气井进行无监督聚类,划分井群;再结合井群的地质和工程参数开展主成分分析,捕获影响产气量波动的关键因素;随后采用极大似然估计方法求解气井所属井群类别,并依托所属类别聚类中心生产数据训练时间卷积神经网络,预测天然气井未来短期内的产气量。结果表明:基于机器学习的气井产量预测模型预测误差平均为5.53%,相较于传统的长短期记忆网络(误差平均为8.98%)和门控循环网络(误差平均为9.06%)预测误差更小,表明该模型能够应用于开发周期相对较短的碳酸盐岩气井的产量预测。研究成果对于机器学习在油气藏开发方面的应用研究具有重要意义。
In view of the problem that the traditional time series model cannot predict the gas yield of natural gas wells with a short production cycle, the affinity propagation algorithm based on machine learning is used to perform unsupervised clustering for natural gas wells and divide the well group. Combined with the geological and engineering parameters of the well group, principal component analysis is carried out to capture the key factors affecting the fluctuation of gas yield. Then, the maximum likelihood estimation method is used to solve the category of well group that the gas well belongs to, and the convolution neural network of training time for the production data of clustering center for the required category is used to predict the gas yield of the natural gas well in the short term. The results show that the yield prediction model of gas well based on machine learning has an average prediction error of 5.53%, and if compared with the long and short-term memory network (with an average error of 8.98%) and gated cyclic network (with an average error of 9.06%), the prediction error is smaller, indicating that this model can be applied for the yield prediction of carbonatite gas well with a relatively short development cycle. The research results are of great significance for the application research of machine learning in oil and gas reservoir development.
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