智能录井技术研究进展及发展展望

王志战

王志战. 智能录井技术研究进展及发展展望[J]. 石油钻探技术,2024,52(5):51−61. DOI: 10.11911/syztjs.2024099
引用本文: 王志战. 智能录井技术研究进展及发展展望[J]. 石油钻探技术,2024,52(5):51−61. DOI: 10.11911/syztjs.2024099
WANG Zhizhan. Research progress and development prospect of intelligent surface logging technology [J]. Petroleum Drilling Techniques, 2024, 52(5):51−61. DOI: 10.11911/syztjs.2024099
Citation: WANG Zhizhan. Research progress and development prospect of intelligent surface logging technology [J]. Petroleum Drilling Techniques, 2024, 52(5):51−61. DOI: 10.11911/syztjs.2024099

智能录井技术研究进展及发展展望

基金项目: 中国石化科技攻关项目“岩屑自动化录井及多元信息在线检测技术研究”(编号:P23158)资助。
详细信息
    作者简介:

    王志战(1969—),男,山东栖霞人,1991年毕业于西北大学岩石矿物学及地球化学专业,2002年获石油大学计算机应用技术专业硕士学位,2006年获西北大学矿产普查与勘探专业博士学位,正高级工程师,长期从事录井基础理论与新技术新方法研究。系本刊编委。E-mail:wangzz.sripe@sinopec.com

  • 中图分类号: P631.81

Research Progress and Development Prospect of Intelligent Surface Logging Technology

  • 摘要:

    录井具有样品条件及制样工序复杂、采集项目多而离散、人工经验依赖性强且人均产值低等特点,亟需加强智能化转型,但相比于其他石油工程技术,智能录井技术进展缓慢,且局限于应用层面。为此,从智能钻井的进展与成效入手,分析了国内外智能钻井在硬件系统、控制系统、应用系统方面的进展与差距;然后,从地质录井、工程录井、智慧平台3个方面分析了智能录井的主要技术进展,包括“数据+”驱动和视觉驱动的岩性识别、流体识别、井下与地面风险识别及预警等。通过对比智能钻井与智能录井的现状,提出智能录井应强化井下智能录井、智能录井机器人等硬件系统及多场数字孪生、多元采集智能控制、多模态录井大模型、智能解释评价等软件系统的研发。同时强调,既要高度重视,又要理性看待智能录井的发展,要在回顾评价、横向对比的基础上,做好战略定位与研发流程优化,实现进度追赶与作用发挥。这些分析与观点,对推动智能录井实现良性、快速发展具有指导意义。

    Abstract:

    Surface logging has the characteristics of complex sample conditions and sample preparation process, numerous and discrete collection items, strong dependence on hands-on experience, and low per capita output. It is urgent to strengthen intelligentialization transformation. However, compared with other petroleum engineering technologies, intelligent surface logging is making slow advancement and facing application limits. Therefore, the progress and gap of hardware systems, control systems, and application systems of intelligent drilling in China and abroad were analyzed from the progress and achievement of intelligent drilling. Then, the main technical progress of intelligent surface logging was analyzed in terms of geological surface logging, engineering surface logging, and intelligent platform, covering “data +” driven and visually driven lithology identification, fluid identification, and downhole and surface risk identification and early alarming. Based on the comparison between intelligent drilling and intelligent surface logging, it was suggested that the research and development of hardware systems such as downhole intelligent surface logging, and intelligent surface logging robots, as well as software systems such as multi-field digital twins, multi-acquisition intelligent control, multimode large model of surface logging, and intelligent interpretation and evaluation should be strengthened. At the same time, it was emphasized that we should attach great importance to and rationally look at the development of intelligent surface logging and determine strategic positioning and process optimization on the basis of retrospective evaluation and horizontal comparison, so as to catch up with the progress and engagement. These analyses and viewpoints have guiding significance in promoting the benign and rapid development of intelligent surface logging.

  • SDCK1井位于四川盆地西北部,是贯彻国家深地战略而部署的万米深地“超级工程”,设计井深万米以上,钻至前震旦系完钻。该区域超深层含多套优质储层,有望发现新的超深层规模天然气增储目标区。盆地内部地下构造变形强烈,地质构造极为复杂,钻井难度居国内首位。针对以上难题,设计了六开六完井身结构,导致SDCK1井上部井眼尺寸超大,以ϕ812.8 mm井眼钻入地层500 m。

    目前,国内大尺寸井眼的井径一般为ϕ444.5 mm、ϕ406.4 mm和ϕ311.1 mm等[15],均远小于SDCK1井的井径,井眼清洁和井壁稳定要求均低于SDCK1井,采用低固相钻井液体系大排量钻进,可以达到快速钻进的目的,而SDCK1井预计机械转速极低。为此,笔者针对ϕ812.8 mm大尺寸井眼易漏易塌、清洁困难和井壁要求高等难题,针对性开展了钻井液体系流变性、抑制性和封堵性研究,形成了可携砂、强封堵、高抑制的钻井液体系,SDCK1井创造了ϕ812.8 mm井眼最深世界纪录,以及ϕ635.0 mm大尺寸套管在国内的首次成功应用。

    SDCK1井设计为六开井身结构,钻头程序为:ϕ914.4 mm+ϕ812.8 mm+ϕ593.73 mm+ϕ444.5 mm+ϕ323.8 mm+ϕ241.3 mm。ϕ812.8 mm大尺寸井眼钻遇地层为剑门关组、蓬莱镇组等,主要存在表层岩层胶结疏松,易井漏和井塌;长段泥岩易水化膨胀,砂岩胶结较差,井壁易失稳。邻井SY001-X7等钻井过程中存在以下问题:1)井壁稳定性差。PY-3井蓬莱镇组井壁垮塌,SY132井、SY001-H2井等的井径扩大率均大于15%。2)井漏频繁。SY001-X7井采用密度为1.05 kg/L的钻井液,分别钻至井深63,137~140和160~162 m时发生井漏。3)地层出水。ST106井采用密度为1.12 kg/L的钻井液钻至79.60 m时地层出水,钻井液密度提高至1.33 kg/L后钻至140 m再次井漏失返。

    SDCK1井ϕ812.8 mm井眼钻井施工时,井下需采用超大尺寸工具,如钻头、稳定器、减振器等需重新研发;井眼大,钻具易横向摆动,导致疲劳失效、扭矩传递慢、机械转速低等;钻具横向摆动破坏滤饼,可能导致井壁失稳[69] 。钻井液施工有以下难点:1)易发生井漏。剑门关组、蓬莱镇组胶结较差,可能发生井漏,部分井段可能有高压地层水。2)环空返速低,井筒难清洁。钻井液循环排量150~170 L/s条件下,钻杆环空返速0.30~0.34 m/s,部分钻屑无法携带出井。3)超大尺寸井眼的井壁稳定性要求高。一般认为坍塌应力与井眼尺寸并无关系[1013],但井眼尺寸减小,抗压强度随之增加,对应的坍塌压力减小[1415];E. Hoek等人[16]认为岩石中含有大量的裂隙、节理等缺陷,尺寸效应的影响较大。SDCK1井大量使用新设备和井下工具,机械钻速将远低于邻井,需要井壁稳定的时间将延长。

    SDCK1井剑门关组为下白垩统的棕红色泥岩夹灰紫色粉砂岩,厚0~300 m;蓬莱镇组为紫红色泥岩、砂质泥岩与灰色粉砂岩不等厚互层,厚300~500 m。剑门关组黏土矿物含量较高,达45%~52%,蓬莱镇组为38%~42%。黏土矿物中,剑门关组伊利石占比43%~60%,伊利石为脆性矿物,易发生水化和坍塌;蓬莱镇组伊/蒙混层占比28%~36%,伊/蒙混层不均匀水化易导致地层失稳。因此,增强钻井液的表面水化抑制能力,减小水化应力。剑门关组、蓬莱镇组泥岩孔隙、裂缝发育,泥岩裂缝开度0.37~2.28 μm(最大约47.0 μm),若钻井液中缺少与地层微裂隙匹配的微米封堵材料,钻井液滤液沿裂缝进入地层使得坍塌压力增大,易导致井壁失稳。

    目前,川西地区已有相应的大尺寸井眼钻井液施工经验[1718],为了适用于超大尺寸井眼,钻井液还需要创新和强化:

    1)精细密度控制。针对表层易漏易涌的情况,强化钻井液密度精细控制,确保每个循环周钻井液密度变化不大于0.01 kg/L;钻进和重浆携砂过程中注意ECD变化,防止压漏地层。

    2)加强流变性。一是提高钻井液的剪切稀释性,环空低剪切下钻井液黏度高,使钻井液呈紊流状态[19-21];二是提高钻井液动塑比(理想动塑比为0.60),以利于悬浮岩屑、携砂,提高井眼清洁能力。

    3)强化抑制性。由于钻遇的剑门关组和蓬莱镇组存在大段泥岩,SDCK1井设计采用无机盐+有机盐+包被剂的抑制体系,增强抑制性,减缓钻井液对地层的渗透和水化作用,提高井壁的稳定性,防止井眼坍塌,从而确保钻井作业安全。

    4)增强封堵性。采用无软化点沥青+惰性封堵颗粒+韧性封堵的多元封堵措施,强化钻井液封堵抑制性能,使封堵剂颗粒满足裂缝开度要求,减少井壁失稳,降低发生卡钻、钻具故障的概率。

    抑制剂分为无机盐抑制剂,如NaCl、KCl等;有机盐抑制剂,如NaCOOH、KCOOH等;包被剂,如K-PAM、FA367等;小阳离子抑制剂,如NW-1等。采用川渝地区蓬莱镇组泥岩掉块,将其粉碎成6/10目,在温度105 ℃条件下烘干24 h。不同抑制剂条件下对烘干岩屑在温度80 ℃下热滚16 h,利用40目筛网测其一次回收率,再热滚16 h后测二次回收率;测试掉块粉末在不同抑制剂条件下的8 h膨胀量,结果见表1

    表  1  不同抑制剂的抑制性试验结果
    Table  1.  Experimental results of inhibitive ability of different inhibitors
    配方 页岩滚动回收率,% 页岩膨胀率,%
    一次 二次
    清水 19.12 3.41 89.41
    1.5%NW-1 42.32 62.14 70.13
    1.0%FZD-5 95.72 92.41 41.19
    1.0%IND30 86.14 81.32 28.37
    7.0%KCl 28.52 17.65 42.00
    10.0%甲酸钾 24.40 16.21 0.94
    10.0%甲酸钾+7.0%KCl 48.43 38.45 34.12
    10.0%甲酸钾+7.0%KCl+0.8%IND30 92.21 85.39 1.43
    10.0%甲酸钾+7.0%KCl+0.5%FZD-5 96.18 93.41 1.21
    10.0%甲酸钾+7.0%KCl+ 0.8%IND30+0.5%FZD-5 99.61 98.12 0.91
    下载: 导出CSV 
    | 显示表格

    从试验结果来看,不复配情况下,1.0%FZD-5二次回收率最高,但分子量较大,对钻井液流变性影响大,其次为1.0%IND30;10.0%甲酸钾的页岩膨胀率最低,仅为0.94%,效果最好。为了提高钻井液的抑制性,将大分子FZD-5、IND30与KCl、KCOOH配合使用,二次回收率可提高至98.12%。考虑FZD-5与IND30的分子链长短,优选抑制剂配方为:0.5%~0.8%FZD-5+0.8%~1.0%IND30+7.0%KCl+10.0%甲酸钾。

    封堵剂可以填充孔喉和裂缝,起到封堵孔缝和改善滤饼质量的作用。目前常用的封堵剂包括:沥青类,如RF-9(乳化沥青)、NFA-25(白沥青)和Soltex;聚合物类,如SX-2(短纤维复合物)、FD-5(变形树脂复合物)及RLC-101;惰性材料,如超细重晶石。

    对上述封堵剂在温度100 ℃条件下热滚24 h,在85 ℃热水条件下用ZL-LT3600+型激光粒度仪测试其激光粒度,还原封堵剂在钻井液中的粒径范围,测试结果见表2

    表  2  不同封堵剂的粒径分布范围
    Table  2.  Particle size distribution of different plugging agents
    堵漏剂 D10/μm D50/μm D90/μm
    3% NFA-25 10.10 45.29 173.06
    3%SX-2 2.11 21.58 90.71
    3%PPL 0.33 0.98 1.67
    超细重晶石 0.36 1.70 3.98
    3%FD-5 4.46 47.60 178.47
    3%Soltex 3.26 56.34 179.23
    2%RLC-101 5.34 89.76 187.32
    下载: 导出CSV 
    | 显示表格

    从测试结果来看,堵漏剂可分为2类。一类为细粒径堵漏剂,D50为0~20 μm,如SX-2、PPL和超细重晶石,为了提高微孔隙的封堵能力,SX-2、PPL封堵剂加量为2%~4%;超细重晶石有提高钻井液密度的作用,加量一般为6%~8%。另一类为粗粒径堵漏剂,D50为40~100 μm,如NFA-25、FD-5、Soltex和RLC-101。

    利用VT6HPIT02-II型可视化液侵度测试仪,测试FD-5等粗粒径封堵剂的封堵能力。在玻璃管内铺入一定量的20/40目测试砂,在测试砂上加入钻井液并在压差0.8 MPa下承压5 min,在测试砂表面上形成滤饼,然后加压至5.0 MPa,根据测试砂的湿润程度,判断钻井液侵入测试砂的深度,评价滤饼的致密程度和材料的封堵效果。试验过程为:基浆为350 mL蒸馏水+ 21.0 g膨润土,转速11 000 r/min搅拌20 min,常温下静置24 h;加入封堵剂,转速11 000 r/min搅拌20 min,常温下静置24 h,高搅10 min,进行钻井液封堵试验,结果见表3

    表  3  不同封堵剂的封堵能力对比
    Table  3.  Comparison of plugging ability of different plugging agents
    配方 封堵滤失量/mL 侵入深度/cm
    基浆
    基浆+2%FD-5 0 5.0
    基浆+2%NFA-25 0 5.4
    基浆+2%Soltex 0 7.0
    基浆+2%RLC-101 0 10.0
    基浆+2%FD-5+2%NFA-25 0 2.0
    下载: 导出CSV 
    | 显示表格

    试验结果评价表明:FD-5、NFA-25封堵效果更优,FD-5与NFA-25配合使用后嵌入深度降为2.0 cm,粒径较为合适;RLC-101、Soltex封堵效果差。为了验证封堵剂对钻井液流变性和滤失量的影响,将不同配方的封堵剂加入钻井液中,转速11 000 r/min搅拌15 min,在温度120 ℃下滚动16 h,出罐后转速11 000 r/min搅拌5 min,测试温度55 ℃时的常规性能和120 ℃条件下的高温高压滤失量(见表4)。

    表  4  不同封堵剂的流变性能试验结果
    Table  4.  Experimental results of rheology of different plugging agents
    配方 密度/
    (kg·L−1
    表观黏度/
    (mPa·s)
    塑性黏度/
    (mPa·s)
    动切力/
    Pa
    静切力/Pa 高温高压滤失量/
    mL
    初切 终切
    基浆 1.65 29.0 27 2.0 0.5 1.5 18.0
    基浆+2%FD-5 1.65 31.5 31 0.5 0.5 2.0 10.0
    基浆+2%NFA-25 1.65 32.5 31 1.5 0.5 2.5 9.6
    基浆+2%NFA-25+2%FD-5 1.65 34.5 32 2.5 1.0 2.5 7.6
    下载: 导出CSV 
    | 显示表格

    试验结果表明,加入2.0%的NFA-25和FD-5封堵剂后,体系的黏切轻微上升,影响较小,高温高压滤失量明显降低。依据封堵剂的封堵原理、粒径和封堵效果,确定封堵剂配方为:2.0%~4.0%NFA-25+2.0%~4.0%FD-5+2.0%~4.0%SX-2+2.0%~4.0%润滑封堵剂PPL+6.0%~8.0%超细重晶石。

    将抑制剂和封堵剂配方固定为基浆条件下,对流型调节剂、降滤失剂等进行加量优化。基浆配方为:4.00%膨润土+0.04%NaOH+0.50%FZD-5+0.80%IND30+7.00%KCl+10.00%甲酸钾+2.00%NFA-25+2.00%FD-5+2.00%SX-2+2.00%润滑封堵剂PPL+6.00%超细重晶石,用重晶石调整至密度为1.30 kg/L。钻井液在转速11 000 r/min下搅拌15 min,在温度50 ℃条件下测试流变性;在温度120 ℃条件下热滚16 h,冷却至50 ℃测试流变性和滤失量,结果见表5

    表  5  钻井液配方优化试验结果
    Table  5.  The test results of drilling fluid formulation optimization
    配方 试验条件 表观黏度/(mPa·s) 塑性黏度/(mPa·s) 动切力/Pa 动塑比 API滤失量/mL
    基浆 热滚前 18.0 14 4.0 0.29
    热滚后 12.0 10 2.0 0.20 8.4
    基浆+2.0%PAC-LV 热滚前 24.5 16 8.5 0.53
    热滚后 21.0 14 7.0 0.50 4.8
    基浆+2.5%PAC-LV 热滚前 26.0 18 8.0 0.44
    热滚后 22.0 15 7.0 0.47 4.2
    基浆+3.0%PAC-LV 热滚前 30.5 24 6.5 0.27
    热滚后 27.0 19 8.0 0.42 3.5
    基浆+0.5%LT-2 热滚前 24.0 15 9.0 0.60
    热滚后 16.0 10 6.0 0.60 8.4
    基浆+1.0%LT-2 热滚前 30.5 18 12.5 0.69
    热滚后 27.0 16 11.0 0.69 7.6
    基浆+1.5%LT-2 热滚前 33.0 19 14.0 0.74
    热滚后 29.0 18 11.0 0.61 6.4
    基浆+2.5%PAC-LV+
    1.5%LT-2
    热滚前 36.0 21 15.0 0.71
    热滚后 32.0 19 13.0 0.68 3.2
    下载: 导出CSV 
    | 显示表格

    表5可以看出,加入2.0%PAC-LV时,钻井液API滤失量已小于5 mL,但为了更好地控制滤失量,优选PAC-LV加量为2.5%;加入LT-2可以提高动切力和降低滤失量,使动塑比达到0.60,建议加量为1.5%。最终配方为:4.00%膨润土+0.04%NaOH+0.50%FZD-5+0.80%IND30+7.00%KCl+10.00%甲酸钾+2.00%NFA-25+2.00%FD-5+2.00%SX-2+2.00% PPL+2.50%PAC-LV+1.50%LT-2,用重晶石调整钻井液密度。

    蓬莱镇组页岩在SDCK1井钻井液体系中的一次回收率为99.54%,二次回收率为97.23%,具有较好的抑制性。将高纯度钠膨润土用滤液浸泡1 h,利用X射线衍射仪测试蒙脱土基底间距,Cu靶,衍射波长为0.154 056 nm,工作电压为40 kV,电流为30 mA,扫描角度2θ 为3°~40°,扫描结果如图1所示。

    图  1  SDCK1井蒙脱石晶层间距
    Figure  1.  Interlayer spacing of montmorillonite crystal in Well SDCK1

    计算得出SDCK1井钻井液的晶层间距为1.30 nm,由于油基钻井液中膨润土晶层间距为1.26 nm,SDCK1井的钻井液抑制性较好。

    SDCK1井钻井液在温度120 ℃下热滚16 h,在转速11 000 r/min下高搅5 min,测激光粒度,粒径分布范围为0.2~100.0 μm。钻井液颗粒粒径分布较广,可对剑门关组等地层的大部分裂缝和钻井液滤饼孔隙进行控制。利用VT6HPIT02-II型可视化液侵度测试仪对钻井液封堵能力进行评价,封堵滤失量为0 mL,嵌入深度为0.40 cm,远低于单一封堵剂的嵌入深度。

    配制预水化24 h以上的膨润土浆和质量分数为 1.0%的聚合物溶液。钻进过程中加入胶液,以维护钻井液性能。强化固控是控制井浆性能的关键,钻进中全程使用振动筛,除砂器、除泥器使用率达85%,离心机使用率为纯钻时间的100%;及时淘洗尖底罐,尽量降低井浆的含砂量和钻屑含量。

    该井段为大井眼,携砂防垮塌是关键;在设备允许的情况下尽可能提高钻井液排量,以提高岩屑上返速度。蓬莱镇组泥岩地层坍塌严重,钻进时钻井液中应加入足量的聚合物,以提高钻井液的抑制防塌能力;若出现井壁失稳,可增大大分子聚合物的加量,也可适当提高钻井液的密度;强化钻井液的包被抑制性能,保证有机盐加量不小于10%,大分子聚合物的加量大于0.5%。

    井场应储备足够量的堵漏剂,以便及时堵漏。中完后,根据井底沉砂情况,采用高黏稠浆或加重钻井液进行间断携砂及垫底,保证套管顺利下入。

    SDCK1井ϕ812.8 mm井眼施工机械钻速低,约为1.0 m/h,耗时30 d。为稳定井壁,加强抑制性和封堵性;钻井液流变性控制较好;为了防止井漏,严格按照ECD控制重浆携砂、严控起下钻速度等,保证了SDCK1井在该开次的顺利完钻。平均井径扩大率为15.49%,井径较为规则,下套管顺利,固井质量较好,电测固井质量:第一界面合格率为 89.99%,第二界面合格率为92.77%。

    钻井施工过程中,为保证大井眼携砂,钻进时钻井液排量为140~160 L/s,出口温度高,钻井液消耗和维护补充量大。为了维护钻井液性能,严格按照配方上限添加KCl、包被剂,根据钻井液性能变化情况加入降滤失剂。现场钻井液配方为:3.0%~4.0%钻井液膨润土浆+0.2%~0.4%NaOH+3.0%~4.0%降滤失剂PAC-LV+1.0%~2.0%降滤失剂LT-2+2.0%~4.0% NFA-25+3.0%~5.0%润滑封堵剂PPL+7.0%~10.0%KCl+10.0%~12.0%有机盐KCOOH+ 0.2%~0.3%SP-80+6.0%~8.0%超细重晶石+0.5%~0.8%聚合物FZD-5+2.0%~4.0%SX-2+加重剂(调整密度至1.20~1.35 kg/L)。钻井液密度为1.10~1.27 kg/L时,塑性黏度为15~30 mPa·s;控制API滤失量,使其小于4 mL;部分井段的钻井液动塑比达到0.45~0.66;ϕ168.3 mm钻杆条件下,钻井液雷诺数分布在1000~1800,未达到紊流,还需要进一步优化改善。

    为消除砂桥和沉砂,采用稠重浆对400 m以深井段重浆携砂,稠重浆密度为1.36~1.40 kg/L,漏斗黏度大于100 s。每40 m重浆携砂一次,控制井底ECD≤1.45 kg/L,未出现井漏等问题。返出的岩屑大多为棱角分明、呈薄片状的泥岩,尺寸为2.50 cm×1.50 cm×0.50 cm左右,返出岩屑量在0.5~1.5 m3之间,保障了井眼畅通。

    针对起下钻阻卡情况,对263~270,381~382,477~483和495~497 m井段进行拉划,阻卡不明显;81~111 m井段的井径扩大率超过35%,层位为剑门关组,分析认为地层较疏松且钻时较大,井壁被长时间冲刷所致。

    1)SDCK1井聚合物钻井液体系抑制性强,晶格间距为1.30 nm;体系中封堵颗粒粒径分布范围广,为0.2~100.0 μm;钻井液性能稳定,满足上部ϕ812.8 mm超大尺寸井眼钻井施工需求。

    2)钻井液施工过程中严格控制ECD,进行重浆携砂、起下钻作业等,未造成井漏,顺利完成了ϕ812.8 mm井眼的钻进、电测、下套管和固井等施工作业。

    3)超大尺寸井眼携砂要求钻井液动塑比严格控制在0.60左右,部分井段低于0.60,建议在现有大尺寸井眼施工经验的基础上,进一步优化钻井液的流变性。

    4)建议进一步研究超大尺寸井眼的钻井液携砂机理和井筒清洁技术,研发和使用携砂纤维等新材料。

  • 图  1   智能钻井系统组成

    Figure  1.   Composition of intelligent drilling system

    图  2   智能录井平台架构

    Figure  2.   Architecture of intelligent logging

    图  3   录井采集技术体系

    Figure  3.   Logging acquisition technology system

    表  1   录井与钻井智慧平台架构的对比

    Table  1   Comparison of intelligent platform architecture between drilling and logging

    层次 智能录井[64] 智能钻完井[1] 钻井[28]
    用户层  移动端、Pad端、PC端的实时监控、技术支持、协同研究、远程决策  钻井工程师、完井工程师、地质工程师、管理人员、管理员
    网络层  局域网、广域网
    应用层  数据管理平台(分类存储与查询等)、数据挖掘平台(统计分析方法)、成果输出平台(岩性自动识别、物性评价、油气层解释、工程智能预警等)  机械钻速智能预测与参数优化、井眼轨迹智能优化与闭环调控、钻井风险智能预警与动态调控、固井质量智能评价与优化控制、压裂方案智能设计和优化调控、完井方案智能设计与生产优化及钻完井多过程动态耦合与多目标协同优化  钻头选型、井壁稳定、钻速预测、卡钻预警、钻井参数优化等
    装备层  智能钻头、井下测量短节、智能导向工具、智能钻杆、智能滑套、智能钻机
    算法层/数据操作层  机理数据融化、数据增强、小样本学习、迁移学习、强化学习、卷积神经网络、小波分析、在线学习、图算法、遗传算法  数据清洗、资源调度、计算工程
    数据层  各类录井仪器、传感器采集的数据、图像、音频/视频等信息  物探数据、综合录井数据、测井数据、岩心数据、地质资料数据、随钻数据、文档资料、其他数据  井信息、录井、测井、地质、地震实时级历时数据
    下载: 导出CSV

    表  2   录井信息处理与解释评价技术体系

    Table  2   Technical system of logging information processing, interpretation, and evaluation

    录井信息处理 解释 评价及应用
    深度校正与数据源深度匹配
    影响因素校正或消除
    散失量恢复或原位重构
    解谱解耦与信息挖掘
    曲线、图谱、影像特征提取
    多元、多维、多尺度数据融合
    平滑、抽稀、插值等处理
    标准化、归一化处理
    岩性、岩相识别
    古生物鉴定及沉积环境识别
    成分及结构、构造识别
    地质层位及地质小层识别
    流体类型及赋存状态识别
    物源、油(气)源识别
    油(气)成因识别
    含水性及水型识别
    有效储层识别
    油气水层解释
    VOCs类型识别
    钻井工况与安全风险识别
    高压层及其成因识别
    溢流预警
    井漏及其原因识别
    物性及孔隙结构评价
    烃源岩特性评价
    脆性、岩石力学、可压性评价
    润湿性、水淹层评价
    含油气丰度及油气性质评价
    含水性或含水率评价
    甜点评价或产能预测
    单井评价或选区评价
    地层压力随钻评价
    可钻性、井壁稳定性评价
    井筒封闭性或盖层评价
    热储及锂、钾、铀丰度评价
    钻井地质设计
    水平井综合地质导向
    压裂选层
    下载: 导出CSV
  • [1] 李根生,宋先知,祝兆鹏,等. 智能钻完井技术研究进展与前景展望[J]. 石油钻探技术,2023,51(4):35–47. doi: 10.11911/syztjs.2023040

    LI Gensheng, SONG Xianzhi, ZHU Zhaopeng, et al. Research progress and the prospect of intelligent drilling and completion technologies[J]. Petroleum Drilling Techniques, 2023, 51(4): 35–47. doi: 10.11911/syztjs.2023040

    [2] 王敏生,光新军. 智能钻井技术现状与发展方向[J]. 石油学报,2020,41(4):505–512. doi: 10.7623/syxb202004013

    WANG Minsheng, GUANG Xinjun. Status and development trends of intelligent drilling technology[J]. Acta Petrolei Sinica, 2020, 41(4): 505–512. doi: 10.7623/syxb202004013

    [3] 李宗田,肖勇,李宁,等. 低油价下的页岩油气开发工程技术新进展[J]. 断块油气田,2021,28(5):577–585.

    LI Zongtian, XIAO Yong, LI Ning, et al. New progress in shale oil and gas development engineering technology under low oil prices[J]. Fault-Block Oil & Gas Field, 2021,28(5): 577–585.

    [4]

    MCCARTHY J, MINSKY M L, ROCHESTER N, et al. A proposal for the Dartmouth summer research project on artificial intelligence: August 31, 1955[J]. AI Magazine, 2006, 27(4): 12–14.

    [5]

    GAINITDINOV B, MESHALKIN Y, ORLOV D, et al. Predicting mineralogical composition in unconventional formations using machine learning and well logging data[R]. IPTC 23487, 2024.

    [6]

    YANG Tao, ARIEF I H, NIEMANN M, et al. A machine learning approach to predict gas oil ratio based on advanced mud gas data[R]. SPE 195459, 2019.

    [7] 陈凯枫,杨学文,宋先知,等. 基于工程录井数据的井漏智能诊断方法[J]. 石油机械,2022,50(11):16–22.

    CHEN Kaifeng, YANG Xuewen, SONG Xianzhi, et al. An intelligent diagnosis method for lost circulation based on engineering logging data[J]. China Petroleum Machinery, 2022, 50(11): 16–22.

    [8] 刘枫. 顺北地区地层四压力智能预测软件研发与应用[D]. 北京:中国石油大学(北京),2023.

    LIU Feng. Research and application of intelligent prediction software for formation four pressure in Shunbei Area[D]. Beijing: China University of Petroleum(Beijing), 2023.

    [9] 匡立春,刘合,任义丽,等. 人工智能在石油勘探开发领域的应用现状与发展趋势[J]. 石油勘探与开发,2021,48(1):1–11. doi: 10.11698/PED.2021.01.01

    KUANG Lichun, LIU He, REN Yili, et al. Application and development trend of artificial intelligence in petroleum exploration and development[J]. Petroleum Exploration and Development, 2021, 48(1): 1–11. doi: 10.11698/PED.2021.01.01

    [10] 王志战. 一体化、智能化时代的录井技术发展方向探讨[J]. 录井工程,2020,31(1):1–6. doi: 10.3969/j.issn.1672-9803.2020.01.001

    WANG Zhizhan. Discussion on the development direction of mud logging technology in the era of integration and intellectualization[J]. Mud Logging Engineering, 2020, 31(1): 1–6. doi: 10.3969/j.issn.1672-9803.2020.01.001

    [11] 王志战. 中国石化录井技术新进展与发展方向思考[J]. 石油钻探技术,2023,51(4):124–133. doi: 10.11911/syztjs.2023027

    WANG Zhizhan. Thoughts for new progress and development directions of Sinopec's surface logging technology[J]. Petroleum Drilling Techniques, 2023, 51(4): 124–133. doi: 10.11911/syztjs.2023027

    [12] 闫铁,许瑞,刘维凯,等. 中国智能化钻井技术研究发展[J]. 东北石油大学学报,2020,44(4):15–21. doi: 10.3969/j.issn.2095-4107.2020.04.003

    YAN Tie, XU Rui, LIU Weikai, et al. Research and development of intelligent drilling technology in China[J]. Journal of Northeast Petroleum University, 2020, 44(4): 15–21. doi: 10.3969/j.issn.2095-4107.2020.04.003

    [13] 张鑫鑫,梁博文,张晓龙,等. 智能钻井装备与技术研究进展[J]. 煤田地质与勘探,2023,51(9):20–30. doi: 10.12363/issn.1001-1986.23.06.0324

    ZHANG Xinxin, LIANG Bowen, ZHANG Xiaolong, et al. Research progress of intelligent drilling equipment and technology[J]. Coal Geology & Exploration, 2023, 51(9): 20–30. doi: 10.12363/issn.1001-1986.23.06.0324

    [14]

    HU Qin, LIU Qingyou. Intelligent drilling: a prospective technology of tomorrow[R]. SPE 103781, 2006.

    [15]

    RASSENFOSS S. Drilling automation: a robot takes over the drilling floor[J]. Journal of Petroleum Technology, 2021, 73(12): 18–22. doi: 10.2118/1221-0018-JPT

    [16]

    LAWRENCE L, REDMOND B, RUSSELL R B, et al. Intelligent wired drill-pipe system provides significant improvements in drilling performance on offshore Australia development[R]. OTC 20067, 2009.

    [17]

    JELLISON M J, PRIDECO G, HALL D R. Intelligent drill pipe creates the drilling network[R]. SPE 80454, 2003.

    [18]

    TURNER D R, HEAD P F, YURATICH M A, et al. The all electric BHA: recent developments toward an intelligent coiled-tubing drilling system[R]. SPE 54469, 1999.

    [19]

    VALVERDE E. Intelligent near-bit reamer affords same-trip drilling, hole enlargement and rathole reduction for optimal deepwater well construction[R]. OTC 28402, 2018.

    [20]

    TILLEY J, NAIR V N, HAMOUDI L. Case study: intelligent RSS improving drilling performance on three mile laterals in the Appalachian Basin[R]. SPE 201723, 2020.

    [21]

    AL ARFI S, ALSOWAIDI F, RUIZ F, et al. New intelligent push-the-bit rotary steerable system helped reducing well time and maximized directional drilling performance, Abu Dhabi, UAE[R]. SPE 207537, 2021.

    [22]

    CORSER G P, HARMSE J E, CORSER B A, et al. Field test results for a real-time intelligent drilling monitor[R]. SPE 59227, 2000.

    [23]

    ZHU J, ZENG L. Intelligent pressure control system on drilling process[R]. OTC 30828, 2020.

    [24]

    SHEN Xinyu, LIU Sen, SU Qiang, et al. Intelligent switch control algorithm of the push-the-bit rotary steerable drilling system[R]. ARMA 20233-0558, 2023.

    [25]

    ROWSELL P J, WALLER M D. Intelligent control of drilling systems[R]. SPE 21927, 1991.

    [26]

    WAN Youwei, LIU Xiangjun, XIONG Jian, et al. Intelligent prediction of drilling rate of penetration based on method-data dual validity analysis[J]. SPE Journal, 2024, 29(5): 2257–2274. doi: 10.2118/217977-PA

    [27]

    ABUGHABAN M, ALSHAARAWI A, MENG Cui, et al. Optimization of drilling performance based on an intelligent drilling advisory system[R]. IPTC 19269, 2019.

    [28]

    XIE Tao, HOU Xinxin, HUO Hongbo, et al. Improving drilling efficiency using intelligent decision system for drilling in Bohai Oilfield based on big data[R]. SPE 215427, 2023.

    [29]

    RASHIDI B, HARELAND G, TAHMEEN M, et al. Real-time bit wear optimization using the intelligent drilling advisory system[R]. SPE 136006, 2010.

    [30]

    WANG Jianhua, GUAN Zhen, LIU Muchen, et al. Drilling stuck probability intelligent prediction based on LSTM considering local interpretability[R]. ARMA 2023-0326, 2023.

    [31] 殷启帅,杨进,曹博涵,等. 基于长短期记忆神经网络的深水钻井工况实时智能判别模型[J]. 石油钻采工艺,2022,44(1):97–104.

    YIN Qishuai, YANG Jin, CAO Bohan, et al. Real-time intelligent rig activities classification model of deep-water drilling using long short-term memory (LSTM) network[J]. Oil Drilling & Production Technology, 2022, 44(1): 97–104.

    [32] 李雪松,张骁,管震,等. 基于图像识别技术的钻井井漏溢流智能报警系统开发[J]. 世界石油工业,2021,28(1):48–54.

    LI Xuesong, ZHANG Xiao, GUAN Zhen, et al. Development of the drilling mud loss and overflow intelligent alarm system based on the image recognition technology[J]. World Petroleum Industry, 2021, 28(1): 48–54.

    [33]

    WANG Han, CHEN Dong, YE Zhihui, et al. Intelligent planning of drilling trajectory based on computer vision[R]. SPE 197362, 2019.

    [34] 张晓东,朱正喜. 智能钻井技术研究[J]. 石油钻采工艺,2010,32(1):1–4. doi: 10.3969/j.issn.1000-7393.2010.01.002

    ZHANG Xiaodong, ZHU Zhengxi. Study of intelligent drilling technology[J]. Oil Drilling & Production Technology, 2010, 32(1): 1–4. doi: 10.3969/j.issn.1000-7393.2010.01.002

    [35]

    de WARDT J. Guest editorial: trends in remote operations and drilling systems automation point to an expanding footprint what comes next and when?[J]. Journal of Petroleum Technology, 2022, 74(11): 10–13. doi: 10.2118/1122-0010-JPT

    [36] 刘合. 油气勘探开发数字化转型人工智能应用大势所趋[J]. 石油科技论坛,2023,42(3):1–9.

    LIU He. Digital transformation of oil and gas exploration and development; unstoppable AI application[J]. Petroleum Science and Technology Forum, 2023, 42(3): 1–9.

    [37] 宋先知,姚学喆,李根生,等. 基于LSTM-BP神经网络的地层孔隙压力计算方法[J]. 石油科学通报,2022,7(1):12–23. doi: 10.3969/j.issn.2096-1693.2022.01.002

    SONG Xianzhi, YAO Xuezhe, LI Gensheng, et al. A novel method to calculate formation pressure based on the LSTM-BP neural network[J]. Petroleum Science Bulletin, 2022, 7(1): 12–23. doi: 10.3969/j.issn.2096-1693.2022.01.002

    [38]

    ROWE H, MAINALI P, NIETO M, et al. Geochemical perspectives on cuttings-based chemostratigraphy and mineral modeling in the Delaware Basin, Texas and New Mexico[R]. URTEC 2019-1068, 2019.

    [39]

    HUSSAIN M, AMAO A, AL-RAMADAN K, et al. A novel method to develop chemostratigraphy using X-ray fluorescence spectral raw data[R]. URTEC 2021-5478, 2021.

    [40]

    MICHAEL N A, SCHEIBE C, CRAIGIE N W. Automations in chemostratigraphy: toward robust chemical data analysis and interpretation[R]. SPE 204892, 2021.

    [41] 唐诚,王崇敬,梁波,等. 基于机器学习算法的页岩气评价参数计算模型研究[J]. 录井工程,2021,32(4):18–22. doi: 10.3969/j.issn.1672-9803.2021.04.003

    TANG Cheng, WANG Chongjing, LIANG Bo, et al. Study on shale gas evaluation parameter calculation model based on machine learning algorithm[J]. Mud Logging Engineering, 2021, 32(4): 18–22. doi: 10.3969/j.issn.1672-9803.2021.04.003

    [42] 刘雨龙. 基于深度学习的岩屑智能分析方法研究[D]. 北京:中国石油大学(北京),2023.

    LIU Yulong. Research on intelligent analysis method of cuttings based on deep learning[D]. Beijing: China University of Petro-leum(Beijing), 2023.

    [43] 夏文鹤,谢万洋,唐印东,等. 砂样岩屑图像特征的岩性智能高效识别[J]. 石油地球物理勘探,2023,58(3):495–506.

    XIA Wenhe, XIE Wanyang, TANG Yindong, et al. Intelligent and efficient lithology identification based on image features of returned cuttings[J]. Oil Geophysical Prospecting, 2023, 58(3): 495–506.

    [44] 张德君,魏伟,刘明艳,等. 基于综合录井数据的地层岩性智能识别方法[J]. 西部探矿工程,2023,35(4):54–59. doi: 10.3969/j.issn.1004-5716.2023.04.016

    ZHANG Dejun, WEI Wei, LIU Mingyan, et al. Intelligent identification method of formation lithology based on comprehensive logging data[J]. West-China Exploration Engineering, 2023, 35(4): 54–59. doi: 10.3969/j.issn.1004-5716.2023.04.016

    [45]

    JACOBS T. Mud-gas breakthrough Equinor develops real-time reservoir-fluid identification[J]. Journal of Petroleum Technolog, 2021, 73(2): 37–39. doi: 10.2118/0221-0037-JPT

    [46]

    HAFIDZ ARIEF I, YANG Tao. Real time reservoir fluid log from advanced mud gas data[R]. SPE 201323, 2020.

    [47]

    YANG Tao, YERKINKYZY G, ULEBERG K, et al. Predicting reservoir fluid properties from advanced mud gas data[J]. SPE Reservoir Evaluation & Engineering, 2021, 24(2): 358–366.

    [48]

    UNGAR F, MCGILL A, NYGAARD M T, et al. Fluid identification from mud gas in the overburden: a case study for the Snorre Field[R]. SPE 214440, 2023.

    [49]

    YANG Tao, ULEBERG K, CELY A, et al. Unlock large potentials of standard mud gas for real-time fluid typing[R]. SPWLA 2022-0007, 2022.

    [50]

    KOPAL M, YERKINKYZY G, NYGÅRD M T, et al. Real-time fluid identification from integrating advanced mud gas and petrophysical logs[R]. SPWLA 2022-0009, 2022.

    [51]

    BUCKLE P S G, ABDULLAH A F H, ZAINI N, et al. Utilization of digitalized numerical model derived from advanced mud gas data for low cost fluid phase identification, derisking drilling and effective completion plan in depleted reservoir[R]. SPWLA 2022-0092, 2022.

    [52]

    WRIGHT A C. Estimation of gas/oil ratios and detection of unusual formation fluids from mud logging gas data[R]. SPWLA 1996-CC, 1996.

    [53]

    MALIK M, HANSON S A, CLINCH S. Maximizing value from mudlogs: integrated approach to determine net pay[R]. SPWLA 5028, 2020.

    [54] 严伟丽,高楚桥,赵彬,等. 基于气测录井资料的气油比定量计算方法[J]. 科学技术与工程,2020,20(23):9287–9292.

    YAN Weili, GAO Chuqiao, ZHAO Bin, et al. Quantitative calculation method of gas-oil ratio in gas logging data[J]. Science Technology and Engineering, 2020, 20(23): 9287–9292.

    [55]

    JIANG Han, DAIGLE H, TIAN Xiao, et al. A comparison of clustering algorithms applied to fluid characterization using NMRT1-T2 maps of shale[J]. Computers & Geosciences, 2019, 126: 52–61.

    [56] 夏文鹤,唐印东,李皋,等. 基于岩屑录井图像的井壁稳定性智能预测方法[J]. 天然气工业,2023,43(12):71–83.

    XIA Wenhe, TANG Yindong, LI Gao, et al. An intelligent prediction method for wellbore stability based on drilling cuttings logging images[J]. Natural Gas Industry, 2023, 43(12): 71–83.

    [57]

    ZHANG Shaohui, HUANG Weihe, BI Guoqiang, et al. Intelligent risk identification and warning model for typical drilling operation scenes and its application[R]. SPE 214599, 2023.

    [58] 胡志强,杨进,王磊,等. 钻井工况智能识别与时效分析技术[J]. 石油钻采工艺,2022,44(2):241–246.

    HU Zhiqiang, YANG Jin, WANG Lei, et al. Intelligent identification and time-efficiency analysis of drilling operation conditions[J]. Oil Drilling & Production Technology, 2022, 44(2): 241–246.

    [59] 张矿生,宫臣兴,陆红军,等. 基于集成学习的井漏智能预警模型及智能推理方法[J]. 石油钻采工艺,2023,45(1):47–54.

    ZHANG Kuangsheng, GONG Chenxing, LU Hongjun, et al. Intelligent early warning model and intelligent reasoning method based on integrated learning for loss circulation[J]. Oil Drilling & Production Technology, 2023, 45(1): 47–54.

    [60] 张敏. 渤海中深层钻井地层三压力智能预测方法研究[D]. 北京:中国石油大学(北京),2023.

    ZHANG Min. Research on the intelligent prediction method of triple pressure in the middle and deep drilling strata in Bohai Sea[D]. Beijing: China University of Petroleum(Beijing), 2023.

    [61] 阎荣辉,黄子舰,杨永强,等. “互联网+” 时代的智慧录井系统应用探索[J]. 录井工程,2020,31(2):1–5. doi: 10.3969/j.issn.1672-9803.2020.02.001

    YAN Ronghui, HUANG Zijian, YANG Yongqiang, et al. Application and exploration of smart logging system in the Internet Plus era[J]. Mud Logging Engineering, 2020, 31(2): 1–5. doi: 10.3969/j.issn.1672-9803.2020.02.001

    [62] 方铁园,马宏伟,郭龙飞,等. 智慧录井平台建设及其在长庆油田的创新应用[J]. 录井工程,2023,34(3):97–101. doi: 10.3969/j.issn.1672-9803.2023.03.015

    FANG Tieyuan, MA Hongwei, GUO Longfei, et al. Construction of smart mud logging platform and its innovative application in Changqing Oilfield[J]. Mud Logging Engineering, 2023, 34(3): 97–101. doi: 10.3969/j.issn.1672-9803.2023.03.015

    [63] 张锦宏,周爱照,成海,等. 中国石化石油工程技术新进展与展望[J]. 石油钻探技术,2023,51(4):149–158.

    ZHANG Jinhong, ZHOU Aizhao, CHENG Hai, et al. New progress and prospects for Sinopec’s petroleum engineering technologies[J]. Petroleum Drilling Techniques, 2023, 51(4): 149–158.

    [64] 梁海波,宋洋,于志刚,等. 钻井液流变性实时测量方法及系统研究[J]. 石油机械,2022,50(1):10–18.

    LIANG Haibo, SONG Yang, YU Zhigang, et al. Real-time measurement method and systematic study on drilling fluid rheology[J]. China Petroleum Machinery, 2022, 50(1): 10–18.

    [65] 孟济良,吴龙斌,张学亭. 岩屑录井的新技术:DL-1型地质录井自动捞砂机介绍[J]. 石油勘探与开发,1984,11(5):75–79.

    MENG Jiliang, WU Longbin, ZHANG Xueting. A new technology of cuttings logging: DL-1 type automatic cuttings acquisition machine[J]. Petroleum Exploration and Development, 1984, 11(5): 75–79.

    [66]

    ALSHEHRI A, KATTERBAUER K, YOUSEF A. Real-time autoregressive deep learning framework for in-line automatic surface logging[R]. SPE 214079, 2023.

    [67]

    LI Sanguo, XIAO Lizhi, LI Xin, et al. A novel NMR instrument for real time drilling fluid analysis[J]. Microporous and Mesoporous Materials, 2018, 269: 138–141. doi: 10.1016/j.micromeso.2017.08.038

    [68] 张志财,刘保双,王忠杰,等. 钻井液性能在线监测系统的研制与现场应用[J]. 钻井液与完井液,2020,37(5):597–601.

    ZHANG Zhicai, LIU Baoshuang, WANG Zhongjie, et al. Development and field application of an online drilling fluid property monitoring system[J]. Drilling Fluid & Completion Fluid, 2020, 37(5): 597–601.

    [69] 王鹏,刘伟,张果. 钻井液性能自动监测装置的现状及改进建议[J]. 钻采工艺,2022,45(3):42–47. doi: 10.3969/J.ISSN.1006-768X.2022.03.08

    WANG Peng, LIU Wei, ZHANG Guo. Status quo and improvement suggestions of automatic monitoring equipment for drilling fluid performance[J]. Drilling & Production Technology, 2022, 45(3): 42–47. doi: 10.3969/J.ISSN.1006-768X.2022.03.08

    [70] 张好林,杨传书,李昌盛,等. 钻井数字孪生系统设计与研发实践[J]. 石油钻探技术,2023,51(3):58–65.

    ZHANG Haolin, YANG Chuanshu, LI Changsheng, et al. Design and research practice of a drilling digital twin system[J]. Petroleum Drilling Techniques, 2023, 51(3): 58–65.

    [71]

    BALACHANDRAN P A, PADMANABHAN K V K. Integrated operations system: implementation of a truly integrated digital oil field and development of digital twin[R]. OTC 32841, 2023.

    [72]

    WANG Peixian, WANG Xiaolin, MA Jun, et al. Digital and intelligent technology in underground gas storage operation based on digital twin technologies[R]. ISOPE I-23-008, 2023.

    [73]

    BUSOLLO C, ABDO E, KATTAR M, et al. Evolution of a digital twin for underground gas storage wells: thermal effects of tubing gas flow on annulus pressure in transient conditions[R]. SPE 220006, 2024.

    [74]

    WANG Peixian, MA Jun, LI Zunzhao, et al. Integrated simulation technology of underground gas storage based on digital twin technologies[R]. ISOPE I-24-043, 2024.

    [75] 刘合,任义丽,李欣,等. 油气行业人工智能大模型应用研究现状及展望[J]. 石油勘探与开发,2024,51(4):910–923.

    LIU He, REN Yili, LI Xin, et al. Research status and application of artificial intelligence large models in the oil and gas industry[J]. Petroleum Exploration and Development, 2024, 51(4): 910–923.

    [76]

    YI M, CEGLINSKI K, ASHOK P, et al. Applications of large language models in well construction planning and real-time operation[R]. SPE 217700, 2024.

    [77]

    PACIS F J, ALYAEV S, PELFRENE G, et al. Enhancing information retrieval in the drilling domain: zero-shot learning with large language models for question-answering[R]. SPE 217671, 2024.

    [78]

    AMEUR-ZAIMECHE O, KECHICHED R, HEDDAM S, et al. Real-time porosity prediction using gas-while-drilling data and machine learning with reservoir associated gas: case study for Hassi Messaoud Field, Algeria[J]. Marine and Petroleum Geology, 2022, 140: 105631. doi: 10.1016/j.marpetgeo.2022.105631

    [79]

    OULADMANSOUR A, AMEUR-ZAIMECHE O, KECHICHED R, et al. Integrating drilling parameters and machine learning tools to improve real-time porosity prediction of multi-zone reservoirs. Case study: Rhourd Chegga Oilfield, Algeria[J]. Geoenergy Science and Engineering, 2023, 223: 211511.

图(3)  /  表(2)
计量
  • 文章访问数:  166
  • HTML全文浏览量:  30
  • PDF下载量:  98
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-05-07
  • 网络出版日期:  2024-10-08
  • 刊出日期:  2024-09-24

目录

/

返回文章
返回