Article(id=1322942738785771594, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322942002035295141, articleNumber=null, orderNo=null, doi=10.13809/j.cnki.cn32-1825/te.2025337, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762617600000, receivedDateStr=2025-11-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793343728014, onlineDateStr=2026-10-30, pubDate=1790352000000, pubDateStr=2026-09-26, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793343728014, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793343728014, creator=13701087609, updateTime=1793343728014, 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=1061, endPage=1070, ext={EN=ArticleExt(id=1322942738974515275, articleId=1322942738785771594, tenantId=1146029695717560320, journalId=1322258508890079423, language=EN, title=Numerical modeling and history matching of deep coalbed methane considering multiple effects, columnId=1322941693024141328, journalTitle=Petroleum Reservoir Evaluation and Development, columnName=Oil and Gas Development, runingTitle=null, highlight=

Deep coal seams are characterized by geological conditions of high in-situ stress, high reservoir temperature, low permeability, and complex tectonic features, resulting in significant differences in gas occurrence states and migration mechanisms compared with shallow coal seams. Conventional commercial numerical simulators are generally based on simplified dual-porosity models, making it difficult to accurately describe the complex stress sensitivity and multiphase flow dynamics of deep coal seams during production and drainage. This leads to considerable discrepancies between simulation results and actual production data. To address this issue, this study established an efficient numerical simulation method suitable for deep coalbed methane (CBM) characteristics, aiming to accurately characterize single-well gas production, water production, bottom-hole flowing pressure, and produced gas composition. Based on the geological and engineering characteristics of deep coal seams, a fully coupled numerical model was developed considering a quasi-triple-porosity medium (matrix pores, microfractures, and large fractures), competitive adsorption/desorption of multicomponent gases (CH4, CO2, etc.), multiscale diffusion, gas-water two-phase seepage, and coal matrix shrinkage/deformation effects. A typical deep CBM Well JS6-7P01 in the Daning-Jixian block of the Ordos Basin was selected as a case study for history matching and analysis. In terms of computational performance, comparisons under different computational conditions showed that, compared to conventional decoupled or simplified simulation methods, the proposed method increased the computation time by only 0.1-0.2 hours, and the number of Newton iterations and linear iterations by only 10.5% under 4-core and 8-core CPU parallel computing conditions. Regarding matching accuracy and geological understanding, the model performed history matching of gas production, water production, bottom-hole flowing pressure, and produced CO2 molar fraction variations for Well JS6-7P01, achieving a comprehensive history fitting rate of 92%. Based on the numerical simulation results, the contribution proportions of free gas and adsorbed gas during production were further quantitatively calibrated, clarifying the sources of productivity contribution in deep CBM reservoirs. Different stages of fluid flow were finely divided. Finally, the seepage characteristics of deep CBM under multi-field coupling effects were characterized based on the evolution patterns of six fields, including pressure field, saturation field, desorption degree field, permeability field, CO2 adsorption mass density field, and CH4 adsorption mass density field. This method overcomes the limitations of conventional numerical simulation methods and provides a preliminary exploration and an effective tool for the prediction and decision-making of deep CBM development.

, articleAbstract=null, authors=XIONGWEI SUN1, 2, HONGYA WANG1, FENGPENG LAI4, HAOQIANG WU3, NAN WU1, 2, authorsList=XIONGWEI SUN, HONGYA WANG, FENGPENG LAI, HAOQIANG WU, NAN WU, authorCompany=null, correspAuthors=HONGYA WANG, 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=1322942742262849628, articleId=1322942738785771594, tenantId=1146029695717560320, journalId=1322258508890079423, language=CN, title=考虑多重效应的深部煤层气数值模型构建及历史拟合研究, columnId=1322941693259022355, journalTitle=油气藏评价与开发, columnName=油气开发, runingTitle=null, highlight=

深部煤层所处的地质环境具有“高地应力、高储层温度、低渗透率”及复杂的构造特征,导致其气体赋存状态及运移机制与浅部煤层存在显著差异。常规商业数值模拟器往往基于简化的双重介质模型,难以准确描述深部煤层在排采过程中复杂的应力敏感性及多相流动态,导致模拟结果与实际生产数据偏差较大。针对这一难题,研究建立了一套适用于深部煤层气特性的高效数值模拟方法,以实现对单井产气、产水、井底流压及组分产出的精确表征。基于深部煤层地质与工程特征,构建了考虑拟三重介质(基质孔隙、微裂隙、大裂缝)、多组分气体(CH4、CO2等)竞争吸附-解吸、多尺度扩散、气水两相渗流以及煤岩基质收缩/变形效应的全耦合数值模型。选取鄂尔多斯盆地大宁—吉县区块的典型深部煤层气井JS6-7P01作为案例进行历史拟合与分析。在算法性能方面,通过对比不同算力环境下的计算表现,在4核CPU与8核CPU并行计算条件下,该方法相较于常规解耦或简化模拟方法,计算耗时仅增加了0.1~0.2 h,牛顿迭代次数和线性迭代次数仅增长了10.5%。在拟合精度与地质认识方面,该模型对JS6-7P01井的产气量、产水量、井底流压以及产出CO2摩尔分数变化进行了历史拟合,综合历史拟合率达92%。基于模拟结果,研究进一步量化标定了排采过程中游离气与吸附气的产出构成比例,明确了深部煤层气的产能贡献来源;精细划分了流体流动的不同阶段,并利用压力场、饱和度场、解吸程度场、渗透率场、CO2吸附质量密度场、CH4吸附质量密度场的“六场”演化规律,表征了深部煤层气在多场耦合作用下的渗流特征。

, articleAbstract=null, authors=孙雄伟1, 2, 王虹雅1, 赖枫鹏4, 吴昊镪3, 武男1, 2, authorsList=孙雄伟, 王虹雅, 赖枫鹏, 吴昊镪, 武男, authorCompany=null, correspAuthors=王虹雅, authorNote=

孙雄伟(1981—),男,博士研究生,教授级高级工程师,主要从事科技管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:

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王虹雅(1981—),女,硕士研究生,工程师,主要从事储量评估管理兼科技项目管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:
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孙雄伟(1981—),男,博士研究生,教授级高级工程师,主要从事科技管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:

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孙雄伟(1981—),男,博士研究生,教授级高级工程师,主要从事科技管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:

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物性参数取值物性参数取值
割理平均孔隙度/%0.9网格尺寸/(m×m×m)20×20×5
x方向裂缝渗透率/10-3 μm20.05煤层厚度/m15
y方向裂缝渗透率/10-3 μm20.05煤层初始压力/MPa20
z方向裂缝渗透率/10-3 μm20.01最大含气量/(m3/t)30
煤层初始含水饱和度/%10吸附气储量/108 m30.78
煤层压缩系数/(10-4/MPa)2.354游离气储量/108 m30.27
井压裂段数11压裂缝渗透率/10-3 μm21 000
井压缝簇数22压裂缝孔隙度/%0.1
CO2初始摩尔分数/%6.3CH4初始摩尔分数/%93.7
CO2吸附量/(mmol/g)2.9CH4吸附量/(mmol/g)1.6
), ArticleFig(id=1322942752211738790, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322942738785771594, language=CN, label=表1, caption=

鄂尔多斯盆地单井JS6-7P01数值模型煤层基础参数

, figureFileSmall=null, figureFileBig=null, tableContent=
物性参数取值物性参数取值
割理平均孔隙度/%0.9网格尺寸/(m×m×m)20×20×5
x方向裂缝渗透率/10-3 μm20.05煤层厚度/m15
y方向裂缝渗透率/10-3 μm20.05煤层初始压力/MPa20
z方向裂缝渗透率/10-3 μm20.01最大含气量/(m3/t)30
煤层初始含水饱和度/%10吸附气储量/108 m30.78
煤层压缩系数/(10-4/MPa)2.354游离气储量/108 m30.27
井压裂段数11压裂缝渗透率/10-3 μm21 000
井压缝簇数22压裂缝孔隙度/%0.1
CO2初始摩尔分数/%6.3CH4初始摩尔分数/%93.7
CO2吸附量/(mmol/g)2.9CH4吸附量/(mmol/g)1.6
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考虑多重效应的深部煤层气数值模型构建及历史拟合研究
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孙雄伟 1, 2 , 王虹雅 1 , 赖枫鹏 4 , 吴昊镪 3 , 武男 1, 2
油气藏评价与开发 | 油气开发 2026,16(5): 1061-1070
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油气藏评价与开发 |油气开发 2026 , 16 (5) : 1061 -1070
考虑多重效应的深部煤层气数值模型构建及历史拟合研究
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孙雄伟1, 2 , 王虹雅1, 赖枫鹏4, 吴昊镪3, 武男1, 2
作者信息
  • 1 中石油煤层气有限责任公司,北京 100028
  • 2 中联煤层气国家工程研究中心有限责任公司,北京 100095
  • 3 中国地质大学(武汉)资源学院,湖北   武汉 430000
  • 4 中国地质大学(北京)能源学院,北京 100083
通讯作者:
王虹雅(1981—),女,硕士研究生,工程师,主要从事储量评估管理兼科技项目管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:
作者简介:

孙雄伟(1981—),男,博士研究生,教授级高级工程师,主要从事科技管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:

Numerical modeling and history matching of deep coalbed methane considering multiple effects
XIONGWEI SUN1, 2 , HONGYA WANG1, FENGPENG LAI4, HAOQIANG WU3, NAN WU1, 2
Affiliations
  • 1 PetroChina Coalbed Methane Company Limited, Beijing 100028, China
  • 2 China United Coalbed Methane National Engineering Research Center Co., Ltd., Beijing 100095, China
  • 3 College of Resources, China University of Geosciences (Wuhan), Wuhan, Hubei 430000, China
  • 4 School of Energy, China University of Geosciences (Beijing), Beijing 100083, China
出版时间: 2026-09-26 doi: 10.13809/j.cnki.cn32-1825/te.2025337
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鄂尔多斯盆地  /  深部煤层气  /  多场耦合  /  数值模拟  /  计算效率  /  历史拟合
Ordos Basin  /  deep coalbed methane (CBM)  /  multi-field coupling  /  numerical simulation  /  computational efficiency  /  history matching
孙雄伟, 王虹雅, 赖枫鹏, 吴昊镪, 武男. 考虑多重效应的深部煤层气数值模型构建及历史拟合研究. 油气藏评价与开发, 2026 , 16 (5) : 1061 -1070 . DOI: 10.13809/j.cnki.cn32-1825/te.2025337
XIONGWEI SUN, HONGYA WANG, FENGPENG LAI, HAOQIANG WU, NAN WU. Numerical modeling and history matching of deep coalbed methane considering multiple effects[J]. Petroleum Reservoir Evaluation and Development, 2026 , 16 (5) : 1061 -1070 . DOI: 10.13809/j.cnki.cn32-1825/te.2025337
中国的煤层气资源类型多样、储量极其丰富,历经三十余年的不懈勘探和开发,该行业已取得显著成就,建成年产量超百亿立方米的产业规模[1]。深部煤层气是指埋藏深度在1 000 m以上的煤层中的天然气,其流动特性受到煤层深度、压力、温度、煤体结构以及煤层含水饱和度等多重因素的影响,成为煤层气勘探与开发的关键领域。深部煤层气的储存状态特点是游离态与吸附态天然气并存。近年来,鄂尔多斯盆地大宁—吉县区块的深部煤层气勘查与开发实现了突破性的进展[2-3],其中,JS6-7P01井的产气量达到10.1×104 m3/d,创下了高产工业气流的记录。
按照中华人民共和国地质矿产行业标准《煤层气储量估算规范》(DZ/T 0216—2020)[4-5],埋深大于1 000 m的煤层气被称为深部煤层气。然而,随着勘探开发的深入,深部煤层气的概念已不仅限于埋深,更强调其高压、高温和游离气与吸附气共存等“深部效应”所带来的复杂地质和工程特征。深部煤层气储层的生产对煤中孔隙压力高度敏感[6-7],且具有低渗透率(0.01~1.00)×10-3 μm2、高初始压力(20~30 MPa)、复杂的储层结构(基质、割理和多重孔隙结构)和独特的存气机制(欠饱和吸附态)等特点,是典型的非常规储层[8-11]。中浅层煤层气的储量以吸附气为主,游离气储量极少,开采过程包括排水降压、解吸和渗流3个阶段[12]。然而深部煤层气中吸附气和游离气同时存在,煤割理中原生水较少,大多为干煤层,开发过程包括游离气产出为主的早期阶段、吸附气和游离气同时产出的中期阶段以及以吸附气产出为主的晚期阶段。目前,已有许多成熟的商业软件和数值模拟技术应用于煤层气储量计算、生产井产量预测和储层物性模拟。传统的煤层气数值模拟一般基于双孔单渗模型,运移的控制方程常采用偏微分方程或方程组[13-14],求解方法主要是有限差分法(FDM)和有限元法(FEM)[15-16];煤基质作为煤层吸附气主要的赋存空间,通过吸附气的解吸扩散至割理中进行汇聚渗流来模拟煤层气的开采[17]。但在深部煤层气的流动模拟中,煤层气运移控制方程具有非线性特点,煤层非均质性强,初始条件和边界条件极其复杂,使煤层气运移成为一种复杂的物理现象。同时,深部煤层表面同样吸附着CO2,并在产出气中测试得到井口CO2呈现出逐渐升高的态势[18],表明采出气中CH4和CO2有着不同规律的吸附-解吸和渗流模式[19-20],但目前的煤层气数值模拟方法无法较好地模拟出这种规律。
基于深部煤层气开发过程中存在的特殊的地质和工程特征,考虑建立了拟三重介质(煤基质、煤割理和压裂缝)、煤质收缩效应、CH4和CO2之间多组分解吸渗流的多场耦合数值模型,完成了数值模型的求解和模拟器研制。该方法应用在鄂尔多斯盆地大宁—吉县区块的单井模型,计算效率高且收敛性好,在三维场中厘清了深部煤层CH4与CO2竞争解吸与渗流的规律,为深部煤层气高效开发提供了理论基础和技术支撑。
为了分析深部煤层不同渗流机理对生产动态的影响因素,研究建立了深部煤层系统内多场耦合的渗流数学模型。模型假设条件为:
1)模拟系统是由基质孔隙系统、割理系统和压裂缝系统构成的“拟三重介质”。
2) 吸附态煤层气仅存在于基质孔隙内,基质是煤层气的存储空间,裂缝-割理系统是流体的流动通道。
3)煤层的气相吸附规律服从Langmuir(朗缪尔)等温吸附模型。
4)煤储层为非均质、各向异性。
5)气、水在煤割理中流动服从Darcy定律。
6)煤层可压缩,水微可压缩流体,气体为真实气体。
7)气井仅与压裂缝、割理系统相连,不考虑基质向井筒中的流动。
在深部煤层生产的气相中,CO2摩尔分数的波动较大,CH4与CO2的竞争吸附解吸和渗流规律需要明确[17]。传统的煤层气模拟采用黑油模型,不考虑气相中的组分,整个气相视为一个气组分。为考虑CO2和CH4的竞争吸附解吸影响,在研究的组分数值模型中,新增两类守恒方程:总物质质量守恒方程和各组分的质量守恒方程。
研究建立的总物质质量守恒方程表示为[17]:
∂∂tφ∑nρnSn+∇⋅∑nρnun=-∑nqn
各组分的质量守恒方程表示为:
∂∂tφ∑nci,nρnSn+∇⋅∑nci,nρnun+∇⋅Ji,n=-∑nqi,n
式中:t为时间,单位s;φ为孔隙度,%;ρn为某相n(气或水)的密度,单位g/cm3;Sn为某相n的饱和度,%;un为某相n的速度张量,单位m/s;qn为某相n的产量,单位kg/(m3·s);ci,n为i组分在某相n里的摩尔分数,%;Ji,n为i组分某相n的扩散量,表示从煤基质表面解吸扩散到煤割理中的量,单位kg/(m3·s);qi,n为i组分某相n的产量,单位kg/(m3·s)。
相速度张量un遵循达西定律,扩散遵循Fick扩散定律[21],其表达式分别为:
un=-kkrnμ∇p-ρg∇Z
Ji=-ρ∑j=1nc-1DijM∂cj∂x+Din∂p∂x
式中:k为岩石的绝对渗透率,单位10-3 μm2;krn为某相n的相对渗透率;ρ为煤层气的密度,单位g/cm3;DijM、Din分别为分子和压力扩散系数,单位m2/s;μ为煤层气黏度,单位mPa·s;p为煤层压力,单位MPa;g为重力加速度,单位m/s2,取值9.81 m/s2;Z为煤层深度,单位m;Ji为i组分的扩散量,单位kg/(m3·s);nc为组分数量;cj为j组分的摩尔分数,%;x为空间上的距离,单位m。
当煤层气存在多组分在煤层上吸附时[22],需要计算原始的吸附气储量,其中每个组分的吸附量按照扩展的Langmuir公式[17,23]进行计算,其表达为:
Lp,y1,y2,…i=cgVipsRTs⋅yi(βp/pi)1+∑j=1ncyj(βp/pj)
式中:cg为气体的压缩系数,单位MPa-1;ps为标况下的压力,单位MPa;Ts为标况下的温度,单位°C;R为气体常数,单位J/(mol·K),取值8.314 J/(mol·K);Vi为i组分的Langmuir体积常数;p为煤层表面的压力,单位MPa;pi为i组分的分压力,单位MPa;pj为j组分的气相压力,单位MPa;β为Langmuir吸附常数,一般取值1.0~1.3;yi为i组分的摩尔分数,%;yj为j组分的摩尔分数,%。
储层压力低于临界解吸压力后,吸附态气体开始解吸,储层孔隙度与渗透率的变化规律有别于传统的应力敏感效应,形成煤层气储层特有的收缩效应。研究利用Palmer-Mansoori的岩石压缩模型[24]进行表征,煤层的体积变化公式为:
cm=ηM-(KM+f-1)r
Vcoalp=Vcoalp01+cmφ0p-p0+1φ0KM-1βp1+βp-βp01+β0
式中:p0、p分别为原始地层压力和目前的地层压力,单位MPa;K为弹性系数,单位MPa;M为杨氏模量,单位MPa;f为煤层的压缩率,一般取值0.5~1.0;r为煤层骨架的压缩性,单位MPa-1;η为几何因子,与割理系统的方向有关,取值0~1.0;β为Langmuir等温线系数,取值0.2~0.5,单位MPa-1;;φ0为煤层初始孔隙度,%;cm为煤岩整体压缩性,单位MPa-1;β0为Langmuir等温线系数初值,取值0.5;Vcoal(p)为目前地层压力下的煤岩体积,单位m3;Vcoal(p0)为原始地层压力下的煤岩体积,单位m3。
煤割理的渗透率变化公式可表示为与孔隙度之间的关系:
ktk0=φtφ0
式中:φ0、φt分别为原始煤层孔隙度和目前的煤层孔隙度,%;k0、kt分别为原始煤层渗透率和目前的煤层渗透率,单位10-3 μm2。
由于煤层孔隙度与煤块的体积成正比关系,因此,将公式(6)代入公式(8)可得到煤割理渗透率随压力而变化的关系:
k=k01+cmφ0p-p0+1φ0KM-1βp1+βp-βp01+β0
煤层的模型采用双重介质模型进行模拟,但压裂缝或天然缝存在时,双重介质则无法较好地表征其特殊的流动性,因此,采用嵌入式离散裂缝(EDFM)[25-27]进行模拟需要考虑建立压裂缝与煤割理(裂缝介质)之间的流动,而压裂缝与煤层之间不考虑流体的交换(图1)。
EDFM既能保留裂缝的几何细节,又可使用块中心网格作为基质网格[28-30]。大多数实际模型中,基质网格采用角点网格,并遵循(i,j,k)的索引方式,易于使用。EDFM不会破坏角点网格的索引方式,可以实现基质网格数据与裂缝数据的分离,从而修改裂缝数据时不用改动基质网格的数据体。
为了求解从离散化过程中获得的上述方程,研制了新一代数值模拟器LandSim对上述耦合数值模型进行求解。数值差分的过程如下:
对于总物质的质量守恒方程,将公式(1)进行离散化处理得到:
Γn,l±12=αklknρnAΔlvnl±12cos2θ
γn,l±12=gρnl±12
Γn,i-12pn,i-1-pn,i-γn,i-12zi-1-zi+Γn,i+12pn,i+1-pn,i-γn,i+12zi+1-zi+Γn,j-12pn,j-1-pn,j-γn,j-12zj-1-zj+Γn,j+12pn,j+1-pn,j-γn,j+12zj+1-zj+Γn,k-12pn,k-1-pn,k-γn,k-12zk-1-zk+Γn,k+12pn,k+1-pn,k-γn,k+12zk+1-zk=Vi,j,kΔtφρnSnt+1-φρnSnt
式中:θ为储层与水平面的夹角,单位(°);α为转换因子;kl为l方向加权调和平均渗透率,单位10-3 μm2;A为横截面积,单位m2;vn为n相的黏度,单位mPa·s;kn为n相的相对渗透率;Γ为一个流量项,该项根据黏度、密度、饱和度和相对渗透率的上游权计算;γ为一个势项,根据网格间的接触面积、重力常数和煤层气黏度获得;Δl为网格间中心点的距离,单位m;p为网格压力,单位MPa;上标t和t+1为时间步长;下标n为第n个网格;下标i,j,k为空间中的3个方向;下标l为2个网格的接触面;z为网格在空间中的深度,单位m。
对于各组分的质量守恒方程,将公式(2)进行离散化处理得到:
Γcn,l±12=αklkrnycnρnAΔlvnl±12cos2θ
ϒcn,l±12=φρnA|l±12
Γcn,i-12pn,i-1-pn,i-γn,i-12zi-1-zi-γcn,i-12DcMyi-1-yi+Dcppi-1-pi+DcTTi-1-Ti+Γcn,i+12pn,i+1-pn,i-γn,i+12zi+1-zi-γcn,i+12DcMyi+1-yi+Dcppi+1-pi+DcTTi+1-Ti+Γcn,j-12pn,j-1-pn,j-γn,j-12zj-1-zj-γcn,j-12DcMyj-1-yj+Dcppj-1-pj+DcTTj-1-Tj+Γcn,j+12pn,j+1-pn,j-γn,j+12zj+1-zj-γcn,j+12DcMyj+1-yj+Dcppj+1-pj+DcTTj+1-Tj+Γcn,k-12pn,k-1-pn,k-γn,k-12zk-1-zk-γcn,k-12DcMyk-1-yk+Dcppk-1-pk+DcTTk-1-Tk+Γcn,k+12pn,k+1-pn,k-γn,k+12zk+1-zk-γcn,k+12DcMyk+1-yk+Dcppk+1-pk+DcTTk+1-Tk=Vi,j,kΔt   φρnSnycnt+1-φρnSnycnt
式中:T为两网格间的传导率,单位m3·(mPa⋅s)/(d·MPa);D为两网格间的扩散率,单位m3/s;上标M代表为组分间扩散;上标p代表气相间扩散;上标T代表网格间扩散;ycn为第n个网格的c组分摩尔分数。
采用隐式方法,在新的时间步长评估左侧的所有流量项。为了求解方程,使用GMRES和Sparselib数值工具进行求解,求解的线性方程组可表示为:
δ=pt+1-pt
Jδ=-R
式中:p为未知量的矢量(主变量);J为雅可比矩阵,包括各个项相对于变量的导数;R为残差矢量;δ为t+1时刻压力与t时刻压力的差值。
以大宁—吉县区块的深部煤层气为例,对现场的单井实际案例进行研究,重点研究深部煤层气数值模拟方法的求解精度、计算效率与历史拟合的吻合程度。
大宁—吉县区块位于鄂尔多斯盆地晋西挠褶带南端与伊陕斜坡东南缘,其中,山西组5号煤层和太原组8号煤层为主体开发煤层[31-32]。根据开采区块的地质资料[33-34],建立了各向异性煤层与煤割理的JS6-7P01单井模型。该井是一口水平井,累产气量为0.24×108 m3,日产气最高达10.1×104 m3,是一口开发效果不错的深部煤层气井,其中压裂缝有11段(图2)。
采用kinetix压裂模拟器对该井进行压裂模拟,一共11段,每段2簇,一共22簇。井周储层的平均泊松比0.19,平均杨氏模量2.5 GPa,平均最小主应力22 MPa,平均最大主应力26 MPa。
建立JS6-7P01单井数值模型,数模的网格尺寸为20 m×20 m×5 m。表1展示了单井数值模型中的关键地质和工程的参数,其中,割理平均孔隙度为0.9%,平均渗透率为0.05×10-3 μm2,平均初始含水饱和度10%,煤基质Langmuir等温吸附的最大吸附量不超过3.3 mmol/g,煤层密度大约为1 420 kg/m3,原始吸附气和游离气储量的比大约为7∶3。裂缝的气水相渗曲线采用室内实验的成果(图3),通过Palmer-Mansoori的岩石压缩模型计算得到的煤割理随孔隙压力变化规律如图4所示。
研究提出的深部煤层气数值模拟方法简写为“多组分-煤质收缩-拟三重介质”,采用新一代数值模拟器LandSim进行计算求解,传统的煤层气“单组分-双重介质”“单组分-煤质收缩-双重介质”采用传统的Eclipse数值模拟器进行计算求解,将2种计算方式的计算效率进行对比分析。分析结果发现:采用4核CPU与8核CPU并行计算,计算得到不同数值模拟方法的计算时间、牛顿迭代次数和线性迭代次数。其中,牛顿迭代次数反映了非线性方程的求解效率,如果牛顿迭代次数过多,说明非线性方程的解比较难找或者模型的设置可能存在问题;线性迭代次数则反映了线性方程的求解效率。
从计算时间对比曲线可以看出(图5a、图5b):“多组分-煤质收缩-拟三重介质”数值模拟方法的总计算时间更长,这是由于组分模型加上拟三重介质的复杂性,增加了牛顿迭代和线性迭代的次数。不过相比传统的模拟器,单井模型的计算时间延长仅0.1~0.2 h,对于历史拟合问题,是在可以接受的范围内。从牛顿迭代次数和线性迭代次数曲线可以看出(图5c、图5d):“多组分-煤质收缩-拟三重介质”数值模拟方法在初期有着较高的迭代次数,这也是总时间较为耗时的原因,相比之下,迭代次数提高了10.5%,验证了该方法的数值计算效率,可以保证较为良好的工程计算。
采用延长注入时间的方式对压裂液进行模拟注入,采用6个月注压裂液,平均日注量64.0 m3,模拟过程中的收敛性得到了极大的改善。对比了“单组分-双重介质”“单组分-煤质收缩-双重介质”和“多组分-煤质收缩-拟三重介质”3种模型的计算结果,与历史动态进行对比。研究井以定产气量进行生产,进行了产气、产水、井底流压和CO2摩尔分数生产的拟合(图6),拟合结果显示“多组分-煤质收缩-拟三重介质”多场耦合模型的拟合程度最高,整体拟合率达92%。
从研究井产水的拟合结果来看(图6b):由于初期模拟压裂液量不足,通过延长注入时间以及增大液量后,产水效果拟合较好,拟合效果大于90%,且返排率达到了76%。历史生产后期产水量递减较快,这是由于气井产气后改善了压裂缝和割理的渗流通道,压裂液的相对渗透率变小导致,因此,部分压裂液还残留在地层中。
从井底流压的拟合结果可以看出(图6c):由于有相当一部分吸附气进行供能,吸附气的解吸能稳住井底压力,后期研究井的流压保持得非常平稳,压力的趋势基本与历史一致。通过历史流压曲线趋势判断,该煤层气生产不同阶段的压力控制因素不同:①第一阶段为煤层割理游离气供气阶段,压降主要来自煤割理中原始的游离气;②第二阶段煤基质吸附气解吸扩散至裂缝供气阶段,压力逐渐稳定。
从研究井测量得到的不同时间点的CO2摩尔分数与数值模拟情况可知(图6d):在实际的历史拟合过程中,通过修正CO2在原始气相中的摩尔分数、扩散系数以及再吸附能力,尝试了拟合开发过程中采出CO2摩尔分数变化的情况,历史拟合的效果较好,能拟合CO2摩尔分数的变化趋势,且能看出CO2产出摩尔分数随开发进行逐渐增大(幅度较小)。
深部煤层气的实际储存量超过了饱和吸附气的数量,部分气体以游离气的形式存在,占据了孔隙和裂隙空间,因此储层的含水饱和度相对较低。根据数值模拟结果标定了游离气和吸附气产气构成(图7),并将深部煤层气的产出分为3个阶段:阶段Ⅰ是游离气主导阶段,产气主要由游离气构成,占比超过75%;阶段Ⅱ是相对稳定的产气阶段,吸附气的产量持续增加,而游离气的产量则有所下降,总产量保持相对稳定,游离气的占比约为40%;阶段Ⅲ是产量递减阶段,吸附气和游离气的产量同时下降,总产气量呈递减趋势,此阶段游离气的占比低于30%。预计在整个生命周期中,游离气产量占比约为30%,将在开发的前2 a内将产出50%的累积产气量。
为表征深部煤层气多场耦合流动的渗流规律,综合应用“六场”刻画地层中煤层气的流动过程(图8):①压力场;②含水饱和度场;③解吸程度场,不同点的解吸程度在压裂前后的变化规律;④缝网场,表征天然裂缝场和人工压裂裂缝场;⑤CH4吸附质量密度场;⑥CO2吸附质量密度场。从水场可以看出:该模型可以刻画气井前期压裂泵注的过程以及改造范围,由于后期开始生产,因此,含水饱和度开始减小,但是仍有部分压裂液残留在地层中。从煤层的解吸场可以看出:煤层中首先从压裂缝周围开始解吸,然后压力漏斗逐步扩散到边界,在该模型中目前该井生产的压力波已触边。煤割理渗透率可以看出压裂前后的变化规律:压裂前在压裂缝改造范围内,煤割理的渗透率也得到了一定程度的改善,然后随着生产的进行,地层压力下降,受到煤质收缩效应的影响,渗透率开始下降。
煤层气的解吸程度是指煤层中的CH4气体被释放(或解吸)的程度,是煤层气生产的关键因素,因为它直接影响到剩余气可采储量以及采收率的计算。因此,研究用数值计算每个网格煤岩的解吸程度,提出计算解吸程度方程为:
Dad=1-VadVad,i
式中:Dad为煤层的解吸程度,%;Vad为某个煤层网格当前时刻下的吸附气量,单位m3;Vad,i为某个煤层网格原始的吸附气量,单位m3。
从CH4和CO2的吸附质量密度场可以看出解吸规律:CH4的解吸过程与解吸场一致,但由于在煤层气的组分模型中,平衡时的吸附质量密度取决于组分的分压,当压裂完成开始生产后,沿着压裂缝,越远离射孔压力越高,而气相中CO2摩尔分数更低,因此,导致CO2分压低,CO2首先在压裂缝的“边部”解吸,“边部”解吸程度大于裂缝的“中部”。
1)建立了考虑拟三重介质、多组分吸附-解吸、扩散、渗流和煤质收缩变形的多场耦合数值模型,并进行数值求解和模拟器研制,该数值模拟方法与常规煤层气模拟方法进行对比,有着较高的计算效率和稳定性。
2)改进的深部煤层气数值模拟方法能够较好地进行生产动态历史拟合,拟合率较高,能准确反映单井的产气、产水、井底压力和产出情况,并能较好地划分产气阶段与流动表征。
3)该模拟方法能够进一步推广至深部煤层气的其他开发井,能够进一步模拟后期的提高采收率措施,比如注CO2置换等,为深部煤层的开发预测与决策进行了初步探索和提供了实质性的有效工具。

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2026年第16卷第5期
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doi: 10.13809/j.cnki.cn32-1825/te.2025337
  • 接收时间:2025-11-09
  • 首发时间:2026-10-30
  • 出版时间:2026-09-26
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  • 收稿日期:2025-11-09
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    1 中石油煤层气有限责任公司,北京 100028
    2 中联煤层气国家工程研究中心有限责任公司,北京 100095
    3 中国地质大学(武汉)资源学院,湖北   武汉 430000
    4 中国地质大学(北京)能源学院,北京 100083

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王虹雅(1981—),女,硕士研究生,工程师,主要从事储量评估管理兼科技项目管理工作。地址:北京市朝阳区丰和大厦,邮政编码:100028。E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
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