文章编号：1000 − 7393(2022)06 − 0784 − 07 DOI: 10.13639/j.odpt.2022.06.019基于CNN-BiGRU 混合神经网络的电潜螺杆泵产液量预测方法贾俊杰1 刘春海1 管桐1 王进修1 刘俊东2 刘金亮1 郝大勇31. 中国石油华北油田分公司工程技术研究院；2. 中国石油华北油田分公司巴彦勘探开发分公司；3. 中国石油冀东油田分公司储气库建设项目部引用格式：贾俊杰，刘春海，管桐，王进修，刘俊东，刘金亮，郝大勇. 基于CNN-BiGRU 混合神经网络的电潜螺杆泵产液量预测方法［J ］. 石油钻采工艺，2022，44（6）：784-790.摘要：为了解决电潜螺杆泵产液量虚拟计量的预测精度和稳定性，提出了一种基于双重注意力机制的卷积神经网络(CNN)和门控循环单元(GRU)混合模型(CNN-BiGRU)。
油井产量预测易受油压、套压及泵工况等因素的影响，先利用皮尔逊相关系数和主成分分析法对数据进行降维，并确定主要影响因素；再利用CNN 网络的局部连接和全局共享来提取历史油井产液量的空间特征；然后将特征送入GRU 网络，提取数据的时间特征；最后利用双重注意力机制为不同的特征赋值对应的权值，进一步提升模型的预测精准度。
将该方法应用到50 000条产液量数据样本上，模型预测精准度RMSE 和MAPE 分别取得5.24%、3.17%。
与GRU 、CNN-GRU 模型相比，所提方法应用效果显著，能有效提升预测精度，具有一定的工程应用价值。
关键词：电潜螺杆泵；产液量预测；混合神经网络；双重注意力机制；卷积神经网络；门控循环单元网络中图分类号：TE32+8;TP183 文献标识码： AA CNN-BiGRU liquid production prediction method for electric submersibleprogressive cavity pumpsJIA Junjie 1, LIU Chunhai 1, GUAN Tong 1, WANG Jinxiu 1, LIU Jundong 2, LIU Jinliang 1, HAO Dayong 31. Engineering Technology Research Institute , PetroChina Huabei Oilfield Company , Renqiu 062552, Hebei , China ;2. Bayan Exploration and Development Branch Company , PetroChina Huabei Oilfield Company , Bayannur 015000, Inner Mongolia , China ;3. Gas Storage Project Department of PetroChina Jidong Oilfield Company , Tangshan 063200, Hebei , ChinaCitation: JIA Junjie, LIU Chunhai, GUAN Tong, WANG Jinxiu, LIU Jundong, LIU Jinliang, HAO Dayong. A CNN-BiGRU liquid production prediction method for electric submersible progressive cavity pumps ［J ］. Oil Drilling & Production Technology,2022, 44(6): 784-790.Abstract: To improve the prediction accuracy and consistency of liquid production virtual metering of electric submersible progressive cavity pumps (ESPCPs), a hybrid model (CNN-BiGRU) integrating convolution neural network (CNN) and bidirectional gate recurrent unit (GRU) was proposed, which also incorporated the dual attention mechanism. This production forecast of oil wells is subjected to tubing pressure, casing pressure and pump conditions. First, the dimensionality reduction of data was performed using Pearson’s correlation coefficient and principal component analysis, and the main influential factors were identified. Subsequently, the local connecting and global sharing of the CNN network was used to extract spatial features of liquid production data of oil wells. The extracted features were then fed to the GRU network to extract the temporal features of the data. Finally, the weights were assigned基金项目: 中国石油华北油田分公司科研项目“华北油田地面系统在线计量技术优化研究”(编号：2022-HB-E05)。
