摘要： 利用机器学习方法预测油田产量的精度与训练样本的代表性及数量息息相关.通常,采用油田生产数据或者油井生产数据构建训练样本.将油田作为训练样本,存在"小样本"的问题;将油井作为训练样本,由于老油田一般具有开发层系多、生产历史长、油井投产批次多等特点,人工标注能够代表油田产量递减规律的训练样本难度大,且耗时费力.本文将油田和油井生产数据有机融合构建训练样本,建立产量智能预测模型,预测油田产量.首先,采用无监督学习的K均值聚类算法,依据有效厚度、孔隙度、渗透率、饱和度等信息对油井进行聚类分析,识别产量递减类别,并将每类油井转换成一口典型油井作为该类油井的代表;其次,将典型井作为预测对象,通过从每类油井中按比例随机抽取油井来增加训练样本数量,即将典型井和油井生产数据融合构建训练样本;最后,基于长短时记忆循环神经网络建立模型预测典型井产量,进而预测油田产量.研究结果表明:该方法既解决了油田数据作为训练样本的"小样本"问题,又降低了油井数据作为训练样本的标注难度与工作量,并且精度符合现场生产要求,对油气产量智能预测的工程化落地应用具有一定指导意义.

关键词: 油井产量, K-Means聚类, 样本标注, 神经网络, 人工智能

Abstract: The accuracy of predicting oilfield production via machine learning algorithms is closely related to the representa-tiveness and quantity of training samples.Generally,oilfield production data or oil well production data are used to construct training samples.There is the"small sample"problem when the oilfield is used for the training samples.To use oil wells as training samples,it is difficult and time-consuming to manually mark training samples that can represent the oilfield production decline,because old oil fields generally have many development layers,long production history and many production batches of oil wells.The production data of oilfield and oil well production data are organically integrated to construct training samples,and the production intelligent prediction model is established to predict the production of the oilfield.Firstly,the K-means clustering algorithm of unsupervised learning is used to perform cluster analysis on oil wells based on effective thickness,porosity,perme-ability,saturation and other observed values,identify the production decline category,and convert each type of oil well into a typical oil well as a representative of this type of oil well.Secondly,typical wells are taken as prediction objects,and the number of training samples is increased by randomly extracting wells proportionally from each type of well,that is,the production data of typical wells and wells are fused to construct training samples.Finally,a model is built based on LSTM neural network to pre-dict the production of typical wells,and then predict the oilfield production.The research results show that this method not only solves the"small sample"problem of oilfield data as training samples,but also reduces the difficulty and workload of labeling oil well data as training samples,and the accuracy meets the requirements of field production,which has certain guiding significance for the engineering application of intelligent prediction of oil and gas production.

Key words: oil well production, K-Means clustering, sample labeling, neural network, artificial intelligence