基于IMA-AmMLP模型的CO_(2)驱最小混相压力预测  

Prediction of minimum miscibility pressure for CO_(2) flooding based on the IMA-AmMLP model

作　　者：骆正山[1] 张景奇 骆济豪 王小完[1] Luo Zhengshan;Zhang Jingqi;Luo Jihao;Wang Xiaowan(School of Management,Xi'an University of Architecture and Technology,Shaanxi Xi'an 710055,China;Ruixin Institute,Beijing Institute of Technology,Beijing 102488,China)

机构地区：[1]西安建筑科技大学管理学院,陕西西安710055 [2]北京理工大学睿信学院,北京102488

出　　处：《石油学报》2024年第10期1522-1528,共7页Acta Petrolei Sinica

基　　金：国家自然科学基金项目(No.41877527)资助。

摘　　要：最小混相压力是衡量油藏能否达到混相驱的标准。为了对最小混相压力进行精准预测,运用改进蜉蝣算法(IMA)优化多层感知机(MLP)的预测模型。运用注意力机制实现对最小混相压力影响因素的提取;通过引入混沌Sobol序列、非线性惯性权重和反向学习的方法增强蜉蝣算法寻优能力,为多层感知机提供最优的权值和阈值,进而构建IMA-AmMLP最小混相压力预测模型;并以吉林油田实际区块为例,对使用效果进行了验证。验证结果表明,IMA-AmMLP模型的预测结果与实际值的拟合度更高,其平均绝对误差为1.036 MPa,平均绝对百分误差为0.024,均方根误差为0.835,均优于原始模型。研究结果表明,IMA-AmMLP模型能够更准确地预测最小混相压力,可以为运用CO_(2)驱开采油藏提供参考。The minimum miscibility pressure(MMP)is a critical parameter that determines whether a reservoir can be explored by miscible flooding.To accurately predict the MMP,the multi-layer perceptron(MLP)prediction model was optimized using an improved mayfly algorithm(IMA).The attention mechanism was used to extract the factors affecting MMP;the optimization capability of IMA was enhanced by incorporating chaotic Sobol sequences,nonlinear inertia weights,and reverse learning methods.These improvements can provide optimal weights and thresholds for the MLP,leading to the establishment of the IMA-AmMLP model for MMP prediction.The model was validated by the case study of a block in Jilin oilfield.The results demonstrate that the IMA-AmMLP model exhibit a higher degree of fitting between the predicted and actual values,with a mean absolute error(MAE)of 1.036 MPa,a mean absolute percentage error(MAPE)of 0.024,and a root mean square error(RMSE)of 0.835,and the values were all superior to those of the original model.This indicates that the IMA-AmMLP model can more accurately predict MMP,providing a valuable reference for the exploitation and management of reservoirs using CO_(2) flooding in fields.

关 键 词：最小混相压力 多层感知机 惯性权重 蜉蝣优化算法 注意力机制 

分 类 号：TE357[石油与天然气工程—油气田开发工程]