摘要
致密油藏油井是国家战略性资源,其产量预测对生产决策优化、采收率提高与开发风险降低上具有重要意义。为解决当下传统算法对油井产量趋势预测准确率低,稳定性差的问题,将GA算法与LSTM算法有机结合,构建出GA-LSTM致密油藏油井产量趋势预测模型。模型首先对OPD12数据进行KNN缺失补充处理,提升数据完整度,同时利用z-score算法去除数据量纲,提高数据交叉计算性能;然后采用LOF算法剔除噪点数据,以提高数据平滑度,在P系数计算下,通过系统降维提升建模效率;接着在LSTM反馈误差的基础上,进行GA适应度评估计算,利用遗传因子提升优势基因占有率,优化输出LSTM的网络初始参数;最后通过PSO寻优算法,在最优参数下,建立GA-LSTM油井产量趋势预测模型。致密油藏油井产量预测仿真结果显示,与RF、BP、SVM与KNN传统产量预测算法相比,提出的GA-LSTM算法的MAPE降低了38.4%,RMSE指标减少了30.63%,R^(2)指标整体提高了2.00%。故综上,提出的GA-LSTM致密油藏油井产量趋势预测算法,在油井产量趋势预测仿真中具有重要的研究价值。
Oil wells in tight reservoirs are national strategic resources,and their production prediction is of great significance to the optimization ofproduction decision,the improvement of recovery factor and the reduction of development risk.In order to solve the problems of low accuracy and poor stability of the traditional algorithm for oil well production trend prediction,this paper combines the GA algorithm with the LSTM algorithm to construct a GA-LSTM tight reservoir oil well production trend prediction model.Firstly,the OPD12 data is processed byKNN missing supplement to improve data integrity,and the z-score algorithm is used to remove data dimensions to improve the performance of data cross-calculation;then the LOF algorithm is used to remove noise data to improve data smoothness,and the modeling efficiency is improved by systemdimensionality reduction under P coefficient calculation;then,based on the feedback error of LSTM,the GA fitness evaluation calculation is carried out,the genetic factor is used to improve the dominant gene occupancy,and the network initial parameters of LSTM are optimized output.Finally,under the optimal parameters,the GA-LSTM oil well production trend prediction model is established by the PSO optimization algorithm.The simulation results show that the MAPE of the proposed GA-LSTM algorithm is reduced by 38.4%,the RMSE index is reduced by 30.63%,and the R^(2) index is increased by 2.00%compared with the traditional production prediction algorithms of RF,BP,SVM and KNN.Therefore,the GA-LSTM tight reservoir oil well production trend prediction algorithm proposed in this paper has important research value in oil well production trend prediction simulation.
作者
李四海
兖鹏
LI Si-hai;YAN Peng(Sinopec Jianghan Oilfield,Qianjang Hubei 433124,China)
出处
《计算机仿真》
2025年第11期257-261,492,共6页
Computer Simulation
关键词
致密油藏油井
长短期记忆网络
遗传参数优化
Tight oil reservoir
Long-term and short-term memory network
Genetic parameter optimization
