基于代理模型的缝内支撑剂铺置形态高效预测方法  

An efficient method for predicting the morphology of proppant packs based on a surrogate model

作　　者：张涛[1] 周航宇[1] 张一凡 郭建春[1] 苟浩然 唐堂 ZHANG Tao;ZHOU Hangyu;ZHANG Yifan;GUO Jianchun;GOU Haoran;TANG Tang(National Key Laboratory of Oil&Gas Reservoir Geology and Exploitation,Southwest Petroleum University,Chengdu 610500,China;PipeChina Zhejiang Pipeline Network Co.,Ltd.,Hangzhou 310000,China;China Petroloilproduction Plant No.7 Changqing Oilfield Company,Xian 710000,China;Shale Gas Research Institute,Petro China Southwest Oil&Gasfield Company,Chengdu 610056,China)

机构地区：[1]西南石油大学“油气藏地质及开发工程”全国重点实验室,成都610500 [2]国家管网集团浙江省天然气管网有限公司,杭州310000 [3]长庆油田分公司第七采油厂,西安710000 [4]中国石油西南油气田公司页岩气研究院,成都610056

出　　处：《工程科学学报》2025年第3期526-537,共12页Chinese Journal of Engineering

基　　金：国家自然科学基金联合基金资助项目(U23B6004)。

摘　　要：非常规油气储层体积压裂中,大量支撑剂颗粒随压裂液注入地层裂缝,其在缝内的铺置形态将决定裂缝支撑效果和导流能力.准确预测缝内支撑剂铺置形态有助于优化压裂设计、提升改造效率.实验模拟和数值模拟是当前复现缝内支撑剂堆积过程和铺置形态的主要手段,但仍存在模拟尺度小、模拟耗时长和操作成本高等局限.本文以支撑剂输送数值模拟结果为数据集,提取了表征支撑剂铺置堆积的特征参数,基于级联神经网络,建立了支撑剂铺置形态预测的智能代理模型.结果表明,代理模型预测结果与数值模拟结果高度吻合,单步预测耗时仅为单步模拟耗时的0.14%.本文提出的模型和方法可实现支撑剂输送仿真加速,极大地缩短了支撑剂铺置形态的预测时间,其进一步完善后将在压裂实践中具有广泛的应用前景.In the volume fracturing of unconventional oil and gas reservoirs,many proppant particles are injected underground along with the fracturing fluid,and their placement patterns determine the propping effect and conductivity of fractures.Accurate prediction of the in-fracture proppant placement patterns can help optimize the fracturing design and improve fracturing efficiency.Currently,experimental and numerical methods are the main approaches for reproducing the proppant accumulation process and placement patterns in fractures.These methods are still confined by limited simulation scales,time-consuming computations,and high-cost operations.In this paper,the two-fluid method was employed for numerical simulations,with a primary focus on the effects of drag,virtual mass,and lift forces on the momentum exchange between phases.The numerical simulations were conducted on the Fluent platform,and the simulation results were validated against experimental data to ensure reliability and accuracy.The numerical simulation results of proppant transport would be adopted as data sets for input,training,and testing.To characterize the intricate accumulation and packing dynamics of proppants,we distilled key parameters,specifically the concentration distribution and accumulation height profiles.Through correlation analysis,the primary factors influencing these characteristic parameters were identified.Intelligent proxy models for the prediction of proppant placement patterns were established on the basis of the cascade neural network,including a time-concentration model for predicting particle volume fraction and a displacement-height model for predicting particle placement height.The former model enabled predictions of the distribution of proppant concentrations within the fracture at different times,whereas the latter allowed estimation of how the stacking heights of proppants varied with the injection rate.Furthermore,the grid precisions of the prediction models were optimized to enhance their accuracy and performance.The data

关 键 词：体积压裂 支撑剂 铺置形态 级联神经网络 代理模型 

分 类 号：TB126[理学—工程力学] TE357[理学—力学]