机理和智能融合下压裂泵压预测及应用  

Pump pressure prediction and application based on mechanism and intelligence

作　　者：李格轩 陈志明[1,2] 胡连博 廖新维 张来斌[1] LI Gexuan;CHEN Zhiming;HU Lianbo;LIAO Xinwei;ZHANG Laibin(College of Petroleum Engineering,China University of Petroleum-Beijing,Beijing 102249,China;College of Petroleum&Geosystems Engineering,The University of Texas at Austin,Austin TX 78712,USA)

机构地区：[1]中国石油大学(北京)石油工程学院,北京102249 [2]美国得州大学奥斯汀分校石油与地质工程学院,得州奥斯汀TX78712,美国

出　　处：《石油科学通报》2024年第4期586-603,共18页Petroleum Science Bulletin

基　　金：国家自然科学基金项目(No.52074322、No.52274046)资助。

摘　　要：我国页岩油气的高效开发离不开大规模压裂技术。页岩油气大规模压裂过程时间长,压裂砂堵事故易发生且后果严重,开展其预警研究对页岩油气压裂施工安全意义重大。然而,目前仍缺乏压裂砂堵主控因素分析及其施工泵压预测的有效手段。针对此问题,考虑压裂机理和泵压变化特征,建立了一套压裂施工过程中泵压实时预测的方法,以开展砂堵预警研究。首先,采用压裂模拟器模拟压裂全过程泵压变化,通过改变不同流体性质与地层参数开展泵压变化规律的主控因素分析,并采用灰色关联分析方法进行主控因素排序。其次,基于断裂力学、支撑剂运移理论和长短时记忆神经网络(LSTM)模型,建立施工泵压预测框架及模型,形成机理和智能融合下的压裂砂堵预警方法,最后基于砂堵预警方法开展了现场压裂砂堵预警实例应用。结果表明,影响典型井施工泵压的因素由主到次依次为排量、流体黏度、主应力差、砂浓度、裂缝簇数及孔眼数。当其他参数不变时,随着流体黏度、主应力差及排量的增大,施工泵压增加;随着裂缝簇数、孔眼数及砂浓度增加,施工泵压降低。将该压裂砂堵预测方法应用于矿场实际,对压裂砂堵事故进行判识和预警,预测砂堵时间较现场人工识别提前19s,得到相对误差约为6.8%。建立的砂堵智能预警方法可靠性较好,预测泵压与现场泵压基本吻合,实现了压裂砂堵精确预警,对页岩油气压裂过程中砂堵预警具有良好的借鉴意义。Efficient development of shale oil and gas in China relies on factory operations and large-scale fracturing technology.Large-scale fracturing of shale oil and gas requires a long time and numerous equipment and facilities,with frequent and severe incidents of fracturing sand blockage.The research on early warning research in these incidents is crucial for the safety of shale oil and gas fracturing operations.However,the effective methods for analyzing the main control factors of fracturing sand block-age and predicting the pump pressure during operations are lacked.To study this issue,considering the fracturing mechanism and pump pressure variation characteristics,a method for real-time prediction of pump pressure during fracturing operations has been established to conduct sand blockage early warning research here.First,a fracturing simulator was used to simulate the entire process of pump pressure changes during fracturing.By altering different fluid properties and formation parameters,the main control factors of pump pressure variation were analyzed,and the grey correlation analysis method was used to rank these factors.Secondly,based on fracture mechanics,proppant transport theory,and the Long Short-Term Memory(LSTM)neural network model,a framework and model for predicting pump pressure during operations was established,forming a method for early warning of fracturing sand blockage under the integration of mechanism and intelligence.Finally,the early warning method for sand blockage was applied to actual field fracturing opera-tions.Results indicate that the factors affecting the pump pressure of a typical well,from most to least significant,are discharge rate,fluid viscosity,differential principal stress,sand concentration,number of fracture clusters,and number of perforations.When other parameters remain constant,as fluid viscosity,differential principal stress,and discharge rate increase,the pump pressure increases;as the number of fracture clusters,perforations,and sand concentration increase,the pump pre

关 键 词：页岩储层 压裂砂堵 断裂力学 智能预警 LSTM模型 

分 类 号：TE319[石油与天然气工程—油气田开发工程] TE357.1