基于集成时域卷积神经网络模型的水驱油田单井产量预测方法  

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

作　　者：张蕾 窦宏恩[1] 王天智[2] 王洪亮 彭翼[1] 张继风[2] 刘宗尚 米兰 蒋丽维 ZHANG Lei;DOU Hongen;WANG Tianzhi;WANG Hongliang;PENG Yi;ZHANG Jifeng;LIU Zongshang;MI Lan;JIANG Liwei(PetroChina Research Institute of Petroleum Exploration&Development,Beijing 100083,China;Research Institute of Petroleum Exploration and Development,Daqing Oilfield Company,Daqing 163000,China)

机构地区：[1]中国石油勘探开发研究院,北京100083 [2]大庆油田勘探开发研究院,黑龙江大庆163000

出　　处：《石油勘探与开发》2022年第5期996-1004,共9页Petroleum Exploration and Development

基　　金：中国石油天然气股份有限公司重大统建项目“中国石油认知计算平台”(2019-40210-000020-02)。

摘　　要：针对水驱油田单井产量变化大、预测难的问题,提出了一种基于时域卷积神经网络(TCN)的水驱油田单井产量预测方法,并进行实例验证。该方法从数据处理入手,依据注水井影响半径衡量油水井对应关系,增加油井当月受注水井影响程度为模型特征,构建随机森林模型填补水驱开发动态数据空缺,根据含水率将单井生产历程划分为低含水、中含水、高含水、特高含水4个阶段,基于TCN建立阶段预测模型,采用麻雀搜索算法(SSA)优化模型超参数,最终将4个阶段模型集成为全生命周期模型用于产量预测。大庆油田应用实践表明:①所用数据处理方法较常规数据处理方法更符合产量数据特点、数据集更具真实性和完备性;②TCN模型较长短时记忆网络(LSTM)等11种时间序列模型预测精度更高;③集成全生命周期模型较单一全生命周期模型可显著降低产量预测误差。Since the oil production of single well in water flooding oilfield varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 10 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models,the model of integrated stages can significantly reduce the error of production prediction.

关 键 词：单井产量预测 时域卷积神经网络 时间序列预测 水驱油藏 

分 类 号：TE312[石油与天然气工程—油气田开发工程]