由于页岩气渗流机理复杂,赋存方式多样,压裂后对裂缝网络的精确识别和表征存在较大困难,现有方法难以准确预测页岩气井产量。为此,提出了机理—数据融合建模的思路,结合连续拟稳态假设、物质平衡方程、产量递减分析方法和递推原理,建立...由于页岩气渗流机理复杂,赋存方式多样,压裂后对裂缝网络的精确识别和表征存在较大困难,现有方法难以准确预测页岩气井产量。为此,提出了机理—数据融合建模的思路,结合连续拟稳态假设、物质平衡方程、产量递减分析方法和递推原理,建立了物理—数据协同驱动的产量预测方法,进而以中国某区块页岩气井现场生产数据为例,对该方法的准确性、可靠性进行了测试,并与经验产量递减分析和时间序列分析方法进行了对比分析。研究结果表明:(1)建立的产能模型采用拟压力代替压力,采用物质平衡拟时间代替时间,弱化了产量、流压和甲烷物性变化带来的影响;(2)以累计产量误差最小为目标开展历史拟合,弱化了生产制度变化带来的影响,使得建立的产能模型能够自动适应流压—产量变化;(3)应用该方法的关键在于采气指数—物质平衡拟时间双对数图中的特征直线,若图中出现特征直线,则可以开展产量预测,反之,则不能预测。结论认为:(1)建立的产量预测方法将不稳定流动问题转化为拟稳态流动问题求解,简化了对储层非均质性的描述,避开了裂缝网络精确识别和定量表征的难题,计算效率高,可解释性强;(2)生产数据测试结果表明该产量预测方法精度高,长期预测结果稳定,并优于Logistic Growth Model、Duong和StretchedExponential Production Decline经验产量递减分析方法,也优于非线性自回归神经网络、长短记忆神经网络时间序列分析方法。展开更多
Fiber is highly escapable in conventional slickwater,making it difficult to form fiber-proppant agglomerate with proppant and exhibit limited effectiveness.To solve these problems,a novel structure stabilizer(SS)is de...Fiber is highly escapable in conventional slickwater,making it difficult to form fiber-proppant agglomerate with proppant and exhibit limited effectiveness.To solve these problems,a novel structure stabilizer(SS)is developed.Through microscopic structural observations and performance evaluations in indoor experiments,the mechanism of proppant placement under the action of the SS and the effects of the SS on proppant placement dimensions and fracture conductivity were elucidated.The SS facilitates the formation of robust fiber-proppant agglomerates by polymer,fiber,and quartz sand.Compared to bare proppants,these agglomerates exhibit reduced density,increased volume,and enlarged contact area with the fluid during settlement,leading to heightened buoyancy and drag forces,ultimately resulting in slower settling velocities and enhanced transportability into deeper regions of the fracture.Co-injecting the fiber and the SS alongside the proppant into the reservoir effectively reduces the fiber escape rate,increases the proppant volume in the slickwater,and boosts the proppant placement height,conveyance distance and fracture conductivity,while also decreasing the proppant backflow.Experimental results indicate an optimal SS mass fraction of 0.3%.The application of this SS in over 80 wells targeting tight gas,shale oil,and shale gas reservoirs has substantiated its strong adaptability and general suitability for meeting the production enhancement,cost reduction,and sand control requirements of such wells.展开更多
文摘由于页岩气渗流机理复杂,赋存方式多样,压裂后对裂缝网络的精确识别和表征存在较大困难,现有方法难以准确预测页岩气井产量。为此,提出了机理—数据融合建模的思路,结合连续拟稳态假设、物质平衡方程、产量递减分析方法和递推原理,建立了物理—数据协同驱动的产量预测方法,进而以中国某区块页岩气井现场生产数据为例,对该方法的准确性、可靠性进行了测试,并与经验产量递减分析和时间序列分析方法进行了对比分析。研究结果表明:(1)建立的产能模型采用拟压力代替压力,采用物质平衡拟时间代替时间,弱化了产量、流压和甲烷物性变化带来的影响;(2)以累计产量误差最小为目标开展历史拟合,弱化了生产制度变化带来的影响,使得建立的产能模型能够自动适应流压—产量变化;(3)应用该方法的关键在于采气指数—物质平衡拟时间双对数图中的特征直线,若图中出现特征直线,则可以开展产量预测,反之,则不能预测。结论认为:(1)建立的产量预测方法将不稳定流动问题转化为拟稳态流动问题求解,简化了对储层非均质性的描述,避开了裂缝网络精确识别和定量表征的难题,计算效率高,可解释性强;(2)生产数据测试结果表明该产量预测方法精度高,长期预测结果稳定,并优于Logistic Growth Model、Duong和StretchedExponential Production Decline经验产量递减分析方法,也优于非线性自回归神经网络、长短记忆神经网络时间序列分析方法。
文摘Fiber is highly escapable in conventional slickwater,making it difficult to form fiber-proppant agglomerate with proppant and exhibit limited effectiveness.To solve these problems,a novel structure stabilizer(SS)is developed.Through microscopic structural observations and performance evaluations in indoor experiments,the mechanism of proppant placement under the action of the SS and the effects of the SS on proppant placement dimensions and fracture conductivity were elucidated.The SS facilitates the formation of robust fiber-proppant agglomerates by polymer,fiber,and quartz sand.Compared to bare proppants,these agglomerates exhibit reduced density,increased volume,and enlarged contact area with the fluid during settlement,leading to heightened buoyancy and drag forces,ultimately resulting in slower settling velocities and enhanced transportability into deeper regions of the fracture.Co-injecting the fiber and the SS alongside the proppant into the reservoir effectively reduces the fiber escape rate,increases the proppant volume in the slickwater,and boosts the proppant placement height,conveyance distance and fracture conductivity,while also decreasing the proppant backflow.Experimental results indicate an optimal SS mass fraction of 0.3%.The application of this SS in over 80 wells targeting tight gas,shale oil,and shale gas reservoirs has substantiated its strong adaptability and general suitability for meeting the production enhancement,cost reduction,and sand control requirements of such wells.
