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Article(id=1322942843332993845, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322942002035295141, articleNumber=null, orderNo=null, doi=10.13809/j.cnki.cn32-1825/te.2025191, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1761494400000, receivedDateStr=2025-10-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343752940, onlineDateStr=2026-10-30, pubDate=1790352000000, pubDateStr=2026-09-26, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343752940, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343752940, creator=13701087609, updateTime=1793343752940, updator=13701087609, issue=Issue{id=1322942002035295141, tenantId=1146029695717560320, journalId=1322258508890079423, year='2026', volume='16', issue='5', pageStart='949', pageEnd='1136', issueExtLink='null', onlineDate='null', pubDate='1790352000000', pubDateStr='2026-09-26', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793343552359, creator='13701087609', updateTime=1793344212649, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322944771945267389, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322942002035295141, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322944771945267390, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322942002035295141, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1122, endPage=1128, ext={EN=ArticleExt(id=1322942845497254711, articleId=1322942843332993845, tenantId=1146029695717560320, journalId=1322258508890079423, language=EN, title=Intelligent optimization of CO2 pre-pad energized fracturing parameters for deep tight oil reservoirs, columnId=1322941869210075380, journalTitle=Petroleum Reservoir Evaluation and Development, columnName=Digital and Intelligent Energy, runingTitle=null, highlight=

Due to high geothermal temperature (152 ℃), high formation pressure, and pronounced stress-shadowing effects, the development of deep tight oil reservoirs in the Dongying Sag (burial depth: 3 700-4 300 m) faces typical challenges such as short fracture half-lengths (<80 m), rapid production decline (decline rate >40% within three months), and significant productivity variations resulting from suboptimal alignment between geological and engineering parameters. This study proposed an enhanced energized fracturing technique of “supercritical CO2 pre-pad injection and precise activation of imbibition fluid”, designed to address the core issues of difficult energy replenishment, restricted fracture propagation, and low mobilization efficiency of matrix crude oil in deep reservoirs. Through phase-state control of supercritical CO2 (temperature: 35 ℃, pressure: 15 MPa, density: 0.8 g/cm³), this technology leveraged its high diffusivity (0.12 cm²/s) and its phase-transition expansion potential for pre-pad energized fracturing and fracture creation. Combined with an optimized anionic petroleum sulfonate imbibition fluid system which reduced interfacial tension to 0.15 mN/m and optimized the contact angle to 65°, it effectively lowered capillary resistance, improved reservoir wettability, and enabled precise activation of matrix crude oil. To tackle the multi-parameter optimization challenge, a numerical simulation database containing 960 schemes was constructed based on an orthogonal experimental design (L64(45)), covering six key geological parameters and five engineering parameters targeted for optimization. Furthermore, a coupled optimization model integrating a back-propagation (BP) neural network and a genetic algorithm (GA) was developed. Taking cumulative oil production as the objective function, global optimization was conducted for CO2 injection volume, imbibition fluid injection volume and injection rate, soaking time, and daily fluid production. Model validation results indicated that the BP neural network achieved a relative prediction error of less than 2.5%. The hidden layer contained 13 nodes for new wells (MSE = 0.003 7) and 8 nodes for old wells (MSE = 0.004 3). The GA converged within 75 generations (crossover probability, Pc=0.4 and mutation probability, Pm=0.2), demonstrating both high accuracy and efficiency. Field applications showed remarkable results. The improved process increased the average fracture half-length from 78 m to 161 m, representing a 106.4% enhancement. The 90-day production decline rate of a typical well Y22 was reduced by 13.3%. After parameter optimization, the productivity of new and old wells increased by 16.8% and 11.2%, respectively. This study establishes a decision-making process integrating numerical simulation, parameter learning, and dynamic optimization. It effectively addresses the challenge of vertical fracture propagation in box-shaped reservoirs and provides a valuable paradigm for the efficient development of unconventional oil and gas resources.

, articleAbstract=null, authors=DIANFA DU, HONGBIN JI, YUJIE TANG, authorsList=DIANFA DU, HONGBIN JI, YUJIE TANG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322942849997742920, articleId=1322942843332993845, tenantId=1146029695717560320, journalId=1322258508890079423, language=CN, title=深层致密油藏前置CO2蓄能压裂参数智能优化, columnId=1322941869369458934, journalTitle=油气藏评价与开发, columnName=数智能源, runingTitle=null, highlight=

东营凹陷深层致密油藏（埋深3 700~4 300 m）因其高地温（152 ℃）、高地层压力及强应力屏蔽效应，开发面临裂缝半长短（小于80 m）、产量递减快（3个月产量递减率超过40%）、地质-工程参数匹配不佳导致的产能差异大等典型深层挑战。本文提出了一种改进的“前置超临界CO₂注入与渗吸液精准激活”蓄能压裂工艺，旨在针对性解决深层能量补充难、裂缝扩展受限与基质原油动用效率低等核心难题。该工艺通过超临界CO₂（温度为35 ℃，压力为15 MPa，密度为0.8 g/cm³）的相态调控，利用其高扩散性（0.12 cm²/s）与相变膨胀潜能前置蓄能造缝；并配合优化的阴离子型石油磺酸盐渗吸液体系（界面张力降至0.15 mN/m，接触角优化至65°），有效降低毛细管阻力、改善储层润湿性，实现基质原油的精准激活。为解决多参数优化难题，研究基于正交试验设计L₆₄（4⁵）构建了包含960组方案的数值模拟样本库，涵盖了6项关键地质参数与5项待优化工程参数。进而，建立了反向传播（BP）神经网络与遗传算法的耦合优化模型，以累积产油量为目标函数，对CO₂注入量、渗吸液注入量、渗吸液注入速度、焖井时间和日产液量进行全局寻优。模型验证表明：BP神经网络预测相对误差不超过2.5%（新井隐层13节点，均方误差为0.003 7；老井隐层8节点，均方误差为0.004 3），遗传算法在75代内收敛（参数交叉概率Pc=0.4，变异概率Pm=0.2），证明了模型兼具高精度与高效性。现场应用效果显著：改进工艺使裂缝半长从78 m增至161 m，增幅达106.4%；典型井Y22的90 d产量递减率降低了13.3%；经参数优化后，新井与老井的产能分别提升了16.8%和11.2%。研究形成了一套集数值模拟、参数学习和动态优化于一体的优化决策流程，有效克服了箱状储层裂缝纵向扩展难题，为非常规油气藏的高效开发提供了可借鉴的优化范式。

, articleAbstract=null, authors=杜殿发, 计洪滨, 汤宇杰, authorsList=杜殿发, 计洪滨, 汤宇杰, authorCompany=null, correspAuthors=null, authorNote=

杜殿发（1972—），男，博士，教授，主要从事油气田开发方向研究。地址：山东省青岛市黄岛区长江西路66号，邮政编码：266580。E-mail：

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杜殿发（1972—），男，博士，教授，主要从事油气田开发方向研究。地址：山东省青岛市黄岛区长江西路66号，邮政编码：266580。E-mail：

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杜殿发（1972—），男，博士，教授，主要从事油气田开发方向研究。地址：山东省青岛市黄岛区长江西路66号，邮政编码：266580。E-mail：

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different fracturing methods for sections Y1 and Y2 in typical well of block X, Shengli Oilfield, figureFileSmall=MIfvUvvIwkZCWmjRTOpIKA==, figureFileBig=8jYj10a5W+A066PvMSgYLw==, tableContent=null), ArticleFig(id=1322942855479698292, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=CN, label=图7, caption=胜利油田X区块典型井第Y1、Y2段不同压裂方式下缝网形态与裂缝半长对比示意图, figureFileSmall=MIfvUvvIwkZCWmjRTOpIKA==, figureFileBig=8jYj10a5W+A066PvMSgYLw==, tableContent=null), ArticleFig(id=1322942855546807157, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=EN, label=Fig.8, caption=Comparison of production curves and oil decline rates of typical Well Y22 under different energized fracturing modes in block X of Shengli Oilfield, figureFileSmall=3FUxkOI+J3bHIvSR+QEjPQ==, figureFileBig=xFRbbR44IX/Q80Q7Ytb0rg==, tableContent=null), ArticleFig(id=1322942855626498934, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=CN, label=图8, caption=不同蓄能压裂模式下胜利油田X区块典型井Y22生产曲线及产油递减率对比, figureFileSmall=3FUxkOI+J3bHIvSR+QEjPQ==, figureFileBig=xFRbbR44IX/Q80Q7Ytb0rg==, tableContent=null), ArticleFig(id=1322942855689413495, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=EN, label=Table 1, caption=Parameter settings, figureFileSmall=null, figureFileBig=null, tableContent=
参数默认值取值
基质渗透率/(10-3 μm2)1.50.5、1.0、1.5、2.0
含油饱和度/%0.650.50、0.55、0.60、0.65
地层厚度/m83、5、8、12
压裂段数51、2、3、5
地层压力/MPa0.80.8、1.0、1.1、1.3
裂缝导流能力/(μm2·cm)2020、30、40、50
CO2注入量/(m3/d)4030、40、50、60
渗吸液注入量/m31 200900、1 000、1 100、1 200
渗吸液注入速度/(m3/d)4030、40、50、60
焖井时间/d148、10、12、14
日产液量/m310080、90、100、110
), ArticleFig(id=1322942855760716664, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=CN, label=表1, caption=

参数设置

, figureFileSmall=null, figureFileBig=null, tableContent=
参数默认值取值
基质渗透率/(10-3 μm2)1.50.5、1.0、1.5、2.0
含油饱和度/%0.650.50、0.55、0.60、0.65
地层厚度/m83、5、8、12
压裂段数51、2、3、5
地层压力/MPa0.80.8、1.0、1.1、1.3
裂缝导流能力/(μm2·cm)2020、30、40、50
CO2注入量/(m3/d)4030、40、50、60
渗吸液注入量/m31 200900、1 000、1 100、1 200
渗吸液注入速度/(m3/d)4030、40、50、60
焖井时间/d148、10、12、14
日产液量/m310080、90、100、110
), ArticleFig(id=1322942855840408441, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=EN, label=Table 2, caption=Geological and production parameters of tight oil reservoir in block X of Shengli Oilfield, figureFileSmall=null, figureFileBig=null, tableContent=
参数新井老井
基质渗透率/(10-3 μm2)1.21.9
含油饱和度/%0.630.51
地层厚度/m1010
压裂段数55
地层压力/MPa1.20.8
裂缝导流能力/(μm2·cm)4~501~20
累积产油量/m37 815.145 636.02
CO2注入量/(m3/d)3550
渗吸液注入量/m31 200900
渗吸液注入速度/(m3/d)6030
焖井时间/d813
日产液量/m310090
), ArticleFig(id=1322942855907517306, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=CN, label=表2, caption=

胜利油田X区块致密油藏地质及生产参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数新井老井
基质渗透率/(10-3 μm2)1.21.9
含油饱和度/%0.630.51
地层厚度/m1010
压裂段数55
地层压力/MPa1.20.8
裂缝导流能力/(μm2·cm)4~501~20
累积产油量/m37 815.145 636.02
CO2注入量/(m3/d)3550
渗吸液注入量/m31 200900
渗吸液注入速度/(m3/d)6030
焖井时间/d813
日产液量/m310090
), ArticleFig(id=1322942855970431867, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=EN, label=Table 3, caption=Optimized parameters for new and old wells, figureFileSmall=null, figureFileBig=null, tableContent=
参数新井老井
CO2注入量/(m3/d)30.8158.81
渗吸液注入量/m31 157.42918.75
渗吸液注入速度/(m3/d)57.5130.49
焖井时间/d8.3012.13
日产液量/m389.5991.40
), ArticleFig(id=1322942856087872380, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=CN, label=表3, caption=

新老井优化参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数新井老井
CO2注入量/(m3/d)30.8158.81
渗吸液注入量/m31 157.42918.75
渗吸液注入速度/(m3/d)57.5130.49
焖井时间/d8.3012.13
日产液量/m389.5991.40
), ArticleFig(id=1322942856154981245, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=EN, label=Table 4, caption=Optimization ranges for new and old wells, figureFileSmall=null, figureFileBig=null, tableContent=
参数新井优化幅度/%老井优化幅度/%
CO2注入量11.9717.60
渗吸液注入量3.582.08
渗吸液注入速度4.151.60
焖井时间3.756.69
日产液量10.411.56
), ArticleFig(id=1322942856247255934, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942843332993845, language=CN, label=表4, caption=

新老井优化幅度

, figureFileSmall=null, figureFileBig=null, tableContent=
参数新井优化幅度/%老井优化幅度/%
CO2注入量11.9717.60
渗吸液注入量3.582.08
渗吸液注入速度4.151.60
焖井时间3.756.69
日产液量10.411.56
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深层致密油藏前置CO2蓄能压裂参数智能优化
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杜殿发 , 计洪滨 , 汤宇杰
油气藏评价与开发 | 数智能源 2026,16(5): 1122-1128
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油气藏评价与开发 |数智能源 2026 , 16 (5) : 1122 -1128
深层致密油藏前置CO2蓄能压裂参数智能优化
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杜殿发（1972—），男，博士，教授，主要从事油气田开发方向研究。地址：山东省青岛市黄岛区长江西路66号，邮政编码：266580。E-mail：

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杜殿发（1972—），男，博士，教授，主要从事油气田开发方向研究。地址：山东省青岛市黄岛区长江西路66号，邮政编码：266580。E-mail：

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杜殿发 , 计洪滨, 汤宇杰
作者信息
  • 中国石油大学（华东）石油工程学院，山东　 　青岛　 　266580
作者简介:

杜殿发（1972—），男，博士，教授，主要从事油气田开发方向研究。地址：山东省青岛市黄岛区长江西路66号，邮政编码：266580。E-mail：

Intelligent optimization of CO2 pre-pad energized fracturing parameters for deep tight oil reservoirs
DIANFA DU , HONGBIN JI, YUJIE TANG
Affiliations
  • School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China
出版时间: 2026-09-26 doi: 10.13809/j.cnki.cn32-1825/te.2025191
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蓄能压裂  /  深层致密油藏  /  超临界CO₂  /  BP神经网络  /  遗传算法  /  参数优化
energized fracturing  /  deep tight oil reservoir  /  supercritical CO2  /  BP neural network  /  genetic algorithm  /  parameter optimization
杜殿发, 计洪滨, 汤宇杰. 深层致密油藏前置CO2蓄能压裂参数智能优化. 油气藏评价与开发, 2026 , 16 (5) : 1122 -1128 . DOI: 10.13809/j.cnki.cn32-1825/te.2025191
DIANFA DU, HONGBIN JI, YUJIE TANG. Intelligent optimization of CO2 pre-pad energized fracturing parameters for deep tight oil reservoirs[J]. Petroleum Reservoir Evaluation and Development, 2026 , 16 (5) : 1122 -1128 . DOI: 10.13809/j.cnki.cn32-1825/te.2025191
正文
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作为非常规油气资源的一种，致密油的高效开发和稳定生产对保证油田增储上产具有重要意义[1]。在致密油生产开发过程中，蓄能压裂是极为有效的开发方式之一。液态CO2作为携砂液具有对储层伤害小、返排迅速等优点[2]，同时能够实现CO2的地质埋存。超临界CO2因其独特的物理性质（如低黏度、高扩散性）和巨大的相变膨胀潜能，成为深层蓄能压裂的理想介质。
压裂施工工艺、开采参数与地层条件不匹配，常导致蓄能压裂井产量差异较大。对于不同油藏，准确优化工程参数对于提高产量至关重要。吴天琛[3]通过数值模拟分析了影响渗吸效率的主控因素；樊建明等[4]和刘杨[5]分别针对注水吞吐提出了技术政策并优化了注采参数。近年来，机器学习方法被引入油气领域，Ma等[6]提出了一套结合数据过采样技术与优化神经网络的预测框架，用于预测地层参数；廖锐全等[7]基于神经网络优化了压裂砂量。然而，现有研究在蓄能改造开采方面，尚未形成针对注入参数的快速优化设计方法。
以东营凹陷X区块沙河街组四段（以下简称沙四段）致密储层为例，其埋深超过3 700 m，为典型深层储层。该储层具有高温（152 ℃）、高压（接近或超过静水压力）、强垂向应力集中等特征，导致其表现出强烈的非均质性。这些典型深层地质条件制约了常规压裂效果，主要表现为能量传递效率低、裂缝形态单一及压裂液性能易失效。为此，本文针对上述问题对常规蓄能压裂工艺进行改进，提出了改进的“前置超临界CO2注入与渗吸液精准激活”蓄能压裂工艺。同时通过构建包含960组方案的数值模拟样本库，建立了反向传播（BP）神经网络-遗传算法（GA）耦合优化模型，实现了地质-工程参数的一体化快速优化，为同类油藏的高效开发提供了技术支撑。
1　区域地质概况
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X区块位于东营凹陷北部构造带东侧，主力含油层系为古近系沙四段，埋深介于3 700~4 300 m，属典型深层致密油藏。
目标储层的关键地质特征及其对开发的影响表现为：①箱状结构与强非均质性，储层累积厚度介于100~300 m，垂向多期叠置，横向相变快，导致应力分布复杂，产生应力屏蔽效应；②储层物性差，平均孔隙度为9.6%，平均渗透率为1.8×10-3 μm2，为低孔特低渗砂砾岩油藏，严重制约流体渗流；③ 典型深层高温高压环境，地层温度高达152 ℃，原始压力系数为1.01，这不仅对压裂液体系的耐温稳定性与携砂性能提出极高要求，也导致岩石力学性质更趋刚性，裂缝在垂向上扩展受到强烈抑制；④ 能量衰竭快，试采井X斜1自喷期仅248 d，产量由20.7 t/d降至0.7 t/d，压裂后地层压力系数降至0.89，反映出深层储层天然能量补给极为有限，对人工蓄能提出了更高需求。
上述地质特征使常规压裂技术在本区应用面临严峻挑战：裂缝扩展受限（有效裂缝半长普遍小于80 m）、产量递减迅速（X斜1井压后3个月产量递减率超过40%）。研究区152 ℃的高地温和超过3 700 m的埋深，为超临界CO2的相态调控提供了有利条件，其高密度和高扩散系数有利于沟通微裂缝、提高波及效率。
2　蓄能压裂工艺原理与数值样本库构建
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2.1　常规蓄能压裂工艺及其局限性
X区块常规蓄能压裂工艺采用清水基渗吸液，存在明显局限性：形成的裂缝半长普遍小于80 m，且60%的井组在压裂后3个月产量递减率超过40%。其核心问题在于：①渗吸液界面张力偏高（1.0 mN/m），导致毛细管阻力大，渗吸效率低下；②储层岩石呈强油湿性（接触角为115°），无法有效启动逆向渗吸作用。
2.2　改进蓄能压裂工艺设计
针对常规工艺的不足，本文提出一种改进的蓄能压裂工艺，工艺流程对比见图1，其核心改进在于超临界CO2相态调控与前置注入和优化渗吸液体系的精准激活。
2.2.1　超临界CO2相态调控与前置注入
在相态控制上，通过井口温压装置将CO2维持在超临界状态（温度为35 ℃，压力为15 MPa，密度为0.8 g/cm³）。此外，目标储层井下特有的深层高温高压条件（温度为152 ℃、压力超过37 MPa）可确保CO2在井下始终处于性质稳定的超临界态，有效规避了液态CO2在深层升温降压环境下易发生剧烈相变导致的井筒压力波动与控制难题。超临界CO2其扩散系数（0.12 cm²/s）是液态CO2的3倍，能更好地克服深层储层的强毛细管阻力，优先侵入微裂缝体系并弱化岩石强度，为后续形成复杂缝网奠定基础。
2.2.2　优化渗吸液体系与精准激活
优化后的渗吸液体系由阴离子型石油磺酸盐（质量分数为0.5%）、醇醚类助溶剂（质量分数为0.1%）和无机盐调节剂（质量分数为1.0%）复配而成。该体系通过双重机制激活基质油：①界面张力调控，将界面张力从清水基的1.0 mN/m显著降至0.15 mN/m，从而大幅降低毛细管阻力；②润湿性反转，使岩石表面从强油湿（接触角为115°）转向中性润湿（接触角为65°），促进毛细管阻力差驱动的逆向渗吸。渗吸液注入量提升至900~1 200 m³，较常规自然渗吸量提高2~3倍。
2.2.3　协同机制
上述两项改进并非简单叠加，而是形成了前置超临界CO2蓄能造缝与后续渗吸液精准激活的协同技术范式。超临界CO2作为先导流体，解决了常规工艺能量分散、难以有效沟通微裂缝的核心问题，为储层创造了高压环境和复杂的缝网结构；优化后的渗吸液则在前者创造的有利条件下，极大降低了毛细管阻力和驱动逆向渗吸的效率，因此，超临界CO2的相态调控是实现深层致密储层有效改造和能量补充的关键前提，渗吸液的优化则是在此基础上对最终采收效果的强化提升。
2.3　数值模拟模型建立
以胜利油田目标区块致密油藏为例，建立以目标水平井为中心的单井数值模拟模型。模拟区域尺寸为550 m × 550 m × 8 m，区域内平均渗透率为1.5×10-3 μm2，平均孔隙度为10%。模型基础参数采用该区块现场实测数据。模型设置平面上网格数为484，x方向和y方向的网格步长均设为25 m，模型总网格节点数为10 533。
渗吸作用的数学模型通过CMG软件中的毛细管阻力变化实现。基于双重介质模型，油水两相压力差由式（1）定义。基质与裂缝系统间的油相压力差和水相压力差分别由式（2）和式（3）计算。
pcow=po-pw
式中：pcow为油水两相压力差，单位MPa；po为油相压力，单位MPa；pw为水相压力，单位MPa。
ΔpoMF=poM-poF-G-pcowM+pcowF 
式中：ΔpoMF为油相基质裂缝压力差，单位MPa；poM为油相基质压力，单位MPa；poF为油相裂缝压力，单位MPa；G为启动压力，单位MPa；pcowM为基质油水两相压力差，单位MPa；pcowF为裂缝油水两相压力差，单位MPa。
ΔpwMF=pwM-pwF-G-pcowM +pcowF 
式中：ΔpwMF为水相基质裂缝压力差，单位MPa；pwM为水相基质压力，单位MPa；pwF为水相裂缝压力，单位MPa。
当基质与裂缝系统间的ΔpoMF > 0时，在毛细管压力差作用下ΔpwMF < 0，导致水相自发吸入基质，同时将原油驱替出，从而实现油水逆向渗吸，机理示意图见图2。
2.4　正交试验设计与样本库生成
为构建智能优化模型的数据基础，本文基于正交试验设计生成数值模拟样本库。选取代表研究区的新钻井和已进行衰竭式生产的老井2种典型井类型。筛选出6项对开发效果影响显著的地质参数（基质渗透率、含油饱和度、地层厚度、裂缝导流能力、压裂段数、地层压力）和5项待优化的关键工程参数（CO2注入量、渗吸液注入量、渗吸液注入速度、焖井时间、日产液量）。采用L₆₄（4⁵）正交表分别设计新井和老井的模拟方案，共构建包含960组方案的样本库。各参数的默认值及取值见表1。
3　基于BP-GA的压裂参数智能优化与应用
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3.1　优化算法原理与耦合策略
目标是通过优化5项工程参数，以最大化累积产油量。鉴于地质-工程参数同油井产量之间存在复杂的非线性关系，采用BP神经网络与GA的耦合模型进行智能优化。该策略的核心在于：利用BP神经网络高精度逼近地质-工程参数与产油量之间的复杂映射关系[8]，继而以训练好的神经网络作为GA的适应度函数，通过GA的全局搜索能力寻找最优参数组合[9-10]。
3.2　BP神经网络预测模型的建立与验证
3.2.1　数据预处理与网格结构
基于构建的960组数值模拟样本库，将样本数据随机划分为训练集（80%，768组）和测试集（20%，192组）。为消除量纲影响，对所有输入参数进行归一化处理，如式（4）所示。
u=u0-uminumax-umin
式中：u为归一化后变量值；u0为变量的原始值；umin为变量的最小值；umax为变量的最大值。
通过试验确定隐层神经元数目：新井案例为13节点（均方误差为0.003 7），老井案例为8节点（均方误差为0.004 3）。采用“tansig”和“purelin”作为激活函数，“trainlm”作为训练算法。
3.2.2　模型性能验证与误差分析
利用测试集对训练好的BP神经网络模型进行验证。如图3所示，神经网络预测产油量与数值模拟产油量在测试集上高度吻合。图4显示训练误差随迭代次数增加迅速下降并趋于稳定。图5进一步表明，模型预测的相对误差最大不超过2.5%，且误差分布集中。这些结果充分证明了所构建的BP神经网络模型具有优异的拟合精度和泛化能力，能够可靠地用于预测累积产油量，可作为GA的适应度函数。
3.3　GA优化实现与结果
3.3.1　GA参数寻优
将训练好的BP神经网络模型的预测输出作为GA的适应度函数。针对GA的关键参数进行了参数敏感性分析，设置交叉概率Pc=0.4，变异概率Pm=0.2，种群大小设置为50，最大进化代数为100。图6展示了新井与老井案例的适应度进化曲线，新井案例在约75代后适应度曲线趋于平稳，老井案例在约50代后收敛，表明GA在此问题中具有较快的收敛速度和良好的全局优化能力。
3.3.2　现场参数优化结果
将现场3口新井和4口老井的平均地质参数（表2）作为固定输入，利用上述BP-GA耦合模型对5项工程参数进行优化。
根据上述现场参数，结合优化算法，将影响因素数据为例输入程序中，求解使得地层累积产油量最大的优化参数组合，得到优化后的工程参数及幅度（表3、表4）。可以看出，该算法优化后的工程参数与原方案误差较小，优化预测具有较好的可行性。
3.4　现场应用效果
3.4.1　储层改造效果
现场应用改进的“前置超临界CO2注入与渗吸液精准激活”工艺后，压裂改造效果显著。以典型水平井Y21为例，对比其Y1、Y2段采用新工艺与常规工艺的效果（图7）。如图7所示，现场压裂改造效果表明：与常规蓄能压裂相比，“前置超临界CO2注入与渗吸液精准激活”的蓄能压裂模式的裂缝发育效果显著增强，其原因是超临界CO2压裂能形成多分支、高粗糙度、微裂缝发育的复杂三维缝网，其超临界态兼具气体低黏度和液体高扩散性，低黏度使其易侵入微裂隙诱发分形扩展，零表面张力与强扩散性深入基质弱化岩石，单相稳定性保障缝网连续延伸[11-13]。而常规蓄能压裂仅形成近似平面主缝，缘于相态不稳定与弱岩石作用，低黏度但滤失失控，能量难传递至裂缝尖端，缺乏化学活性，无法弱化岩石形成微裂缝，相变导致缝网不连续，需依赖支撑剂维持导流能力[14-15]。测量结果显示，新工艺下的裂缝半长从78 m大幅增加至161 m，增幅达106.4%。
3.4.2　生产动态对比
选取典型井Y22，对比其应用优化参数后的改进工艺与原工艺的90 d生产动态，如图8所示，改进工艺采用超临界CO2形成复杂三维缝网，显著扩大泄油面积，使初始产油量达38.2 t/d（常规工艺仅22.5 t/d）。在0~30 d，超临界态低黏度CO2侵入基质沟通微裂缝，延缓早期产油量的递减，随后磺酸盐渗吸液通过毛细管压力差驱动逆向渗吸激活基质原油，在>30~60 d持续置换吸附态原油。最后超临界CO2相变膨胀效应与溶解增流作用持续进行能量补给，保障>60~90 d仍维持6.7 t/d稳产（常规仅0.6 t/d）。同时可以发现常规工艺90 d产油量从22.50 t/d衰减至0.35 t/d（98.4%递减），改进工艺产油量从38.20 t/d衰减至5.68 t/d（85.1%递减），该工艺产油递减率降低13.3%。
将表3所示的优化工程参数应用于现场新井和老井（控制地质因素不变），对比优化前后的累积产油量。应用优化参数后，新井的产能提升了16.8%，老井产能提升了11.2%，证明了本文所提智能优化方法的有效性和实用价值。
4　结论
收起
1） 改进的“前置超临界CO2注入与渗吸液精准激活”蓄能压裂工艺有效攻克了深层致密油藏能量补充难与渗吸效率低的双重瓶颈。该工艺利用超临界CO2的高扩散性（0.12 cm²/s，为液态CO2的3倍）与相变膨胀潜能前置蓄能造缝，耦合优化磺酸盐渗吸液体系（界面张力降至0.15 mN/m，接触角优化至65°）降低毛细管阻力并改善润湿性。现场应用后，裂缝半长由78 m增至161 m（增幅106.4%），典型井Y22的90 d产量递减率降低13.3%，实现了复杂缝网构建与基质原油高效动用的协同目标。
2） 构建的BP神经网络-GA耦合优化模型兼具高精度与高效收敛性。BP神经网络对累积产油量的预测相对误差不超过2.5%（新井隐层13节点，均方误差为0.003 7；老井隐层8节点，均方误差为0.004 3），并以之为适应度函数，GA在75代内收敛（Pc=0.4，Pm=0.2）。优化参数现场应用后，新井与老井产能分别提升16.8%和11.2%，验证了模型在不同井况下的泛化能力与实用价值。
3） 构建了融合6项地质参数与5项工程参数的“数值模拟-参数学习-动态优化”一体化决策流程，有效解决了东营凹陷沙四段箱状深层致密储层（埋深3 700~4 300 m）裂缝纵向扩展受限的工程难题。该流程为页岩气、煤层气等非常规油气资源的高效开发提供了可复制推广的技术范式。

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2026年第16卷第5期
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doi: 10.13809/j.cnki.cn32-1825/te.2025191
  • 接收时间：2025-10-27
  • 首发时间：2026-10-30
  • 出版时间：2026-09-26
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  • 收稿日期：2025-10-27
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    中国石油大学（华东）石油工程学院，山东　 　青岛　 　266580
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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