Research Progress and Development Prospect of Intelligent Surface Logging Technology
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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.
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Keywords:
- Intelligent surface logging /
- intelligent drilling /
- robots /
- digital twin /
- large model /
- intelligent control
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经过多年开发，渤海油田已进入注水开发阶段。截至2019年1月，注水井多达800余口，分层注水率约为96%，注水开发效果关系到油田的持续稳产、增产。但是，近些年随着注水井大幅度增加以及该油田对后期调配要求的不断提高，常规分层注水工艺（空心集成、同心分注和地面分注等）存在的问题逐渐暴露出来，如常规分层注水工艺测调作业占用井口时间长，影响平台其他作业；调配效率和合格率低；管柱不具备反洗井功能[1-7]。
为解决渤海油田分层注水井存在的问题，采用了自提升式反洗井分层注水工艺、智能分层注水工艺等[8-11]，均取得了一定效果，但这些分层注水工艺的适用性、可靠性普遍较差。为此，笔者结合渤海油田注水井的地层条件、完井方式等，借鉴国内成熟的分层注水技术[12-16]，研发了可反洗测调一体分层注水工艺。现场应用表明，该分层注水工艺在大幅度提高测调效率的基础上，可实现不动管柱反洗井，应用效果良好。
1. 常规分层注水工艺存在的问题
渤海油田应用的常规分层注水工艺主要有投捞式分注（空心集成、同心分注）和地面分注等[1-7]，其中投捞式分层注水工艺是利用钢丝反复投捞井下水嘴进行分层调配，地面分层注水工艺是通过地面调节不同注入管汇的流量实现井下分层调配。这些常规分层注水工艺主要存在以下问题：
1）无反洗通道，无法实现不动管柱反洗井作业。海上油田由于受空间限制，生产水处理流程较短，停留时间短，注入水水质波动较大，长期注水容易导致井筒及近井地带堵塞。定期进行反洗井作业可以将井筒附近污染物及时冲洗至地面，既能减缓井筒及近井地带堵塞，降低注水压力，又可以防止污染物及地层出砂卡住注水管柱。但常规分层注水管柱多采用“定位密封+配水器+插入密封”的结构，尚不具备反洗井功能，无法满足海上日益迫切的不动管柱反洗井需求。
2）测调效率低，影响平台其他作业。常规投捞式分层注水工艺测调时，需要利用钢丝反复投捞水嘴，导致调配效率低，平均单井调配时间长达3～4 d；测调精度低，调配合格率仅有80%，而且测调作业时大量占用平台有限的空间和施工时间，影响了平台上其他作业。近些年，随着注水井数量增多和分层注水管理要求的不断提高，常规分层注水工艺已无法满足现场应用需求。
3）套管带压注水，不符合安全注水要求。地面分层注水工艺可以实现地面实时测调，无需井口作业，但该工艺采用的注水管柱结构复杂，需要套管带压注水，而长期带压注水容易对套管造成损伤。另外，该工艺最多只能实现3层注水，对于注水层位较多的井适应性差。
2. 可反洗测调一体分层注水工艺
2.1 工艺原理
针对常规分层注水工艺存在的问题，研发了可反洗测调一体分层注水工艺，主要通过入井电缆为测调仪供电，并传输数据、指令，其工艺原理如图1所示。
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图 1 可反洗测调一体分注工艺原理示意Figure 1. The principle of integrated reverse washing, measuring and adjusting zonal water injection process测调仪与配水器（水嘴内置）对接后，采用边测边调的方式进行流量测试与调配。通过地面仪器监测流量压力曲线，实时调节注水阀水嘴开度，无级调节，直至达到配注流量。工具一次下井即可完成所有层段测试和调配。
2.2 管柱结构
可反洗测调一体化管柱采用了分层防砂、分层注水一体化的设计理念，由外层的分层防砂管柱和内层的分层注水管柱组成，分层防砂管柱主要由顶部封隔器、隔离封隔器、筛管、盲管和油管锚组成，分层注水管柱主要由注水封隔器、测调一体配水器和反洗阀等组成（见图2）。分层防砂管柱和分层注水管柱分体设计，分层注水管柱可单独检换[5-7]。
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图 2 可反洗测调一体工艺管柱Figure 2. The integrated reverse washing, measuring and adjusting pipestring2.3 工艺参数
可反洗测调一体分层注水工艺包括分层防砂管柱下入、分层防砂管柱验封、分层注水管柱下入、分层注水管柱验封和分层注水管柱测调等工艺过程。该工艺针对海上油田ϕ177.8 mm和ϕ244.5 mm套管射孔井研制，满足渤海油田多层、大排量注水的需求。具体的工艺参数为：流量<500 m3/d，井斜角≤60°，井温<140 ℃，工作压差<35 MPa，分层数<8层，调配合格率≥90%。
3. 反循环洗井工具及管柱设计
3.1 反循环洗井工具
反循环洗井工具的关键部件是防蠕动密闭自锁封隔器，其结构如图3所示，主要包括防蠕动机构、密闭自锁机构和解封机构。防蠕动机构是由第一胶筒、液缸和活塞构成独立的密闭压力系统，注水时，水流经上液孔推动液缸上移，挤压液压油，使第一胶筒膨胀坐封，第一胶筒承受管柱的蠕动力。密闭自锁机构的工作原理为：注水时，水流经下液孔进入，挤压第二胶筒膨胀坐封，同时液压力释放单向阀；停注后，单向阀自动关闭下液孔，将液压力密闭在第二胶筒内，第二胶筒始终处于坐封状态。解封机构：反洗井时，油套环空的压力液由反洗进液孔进入，打开下液压孔，密闭在第二胶筒内的液压力释放，第二胶筒解封。
3.2 反循环洗井管柱
注水时，防蠕动密闭自锁封隔器坐封，实现分层注水。反洗井时，通过油套环空加压，使防蠕动密闭自锁封隔器解封，洗井液进入防砂层段。进入防砂层段的洗井液，一部分进入筛管与套管环空，清洗筛网与炮眼；另一部分进入注水管柱与筛管环空，清洗配水器水嘴和管壁。最后，洗井液经洗井阀进入中心油管返至地面。反循环洗井管柱如图4所示。
4. 现场应用
可反洗测调一体分注工艺自2018年开始现场应用以来，已累计应用几十井次，取得了很好的应用效果。其中，10口注入困难的井进行了不动管柱反洗井作业，反洗井后各井的注水能力均得到了不同程度的提升，延长了酸化周期（平均可延长2个月）；此外，完成了30井次的调配作业，平均单井调配工期仅需10 h，相较常规投捞式分层注水工艺2～3 d的调配工期，测调效率大幅提高。下面以A井为例具体介绍其应用情况。
渤海油田A井分6层注水，最大井斜角42.8°，部分注水层位因砂埋注不进水，决定采用“大修打捞+补射孔+分层防砂+分层注水”的方式恢复注水，后期“分层防砂+分层注水”部分采用可反洗测调一体化分层注水工艺。分层防砂管柱和分层注水管柱均顺利入井，分层防砂管柱和分层注水管柱验封均合格。分层注水初期，对A井进行了模拟测调。
4.1 测调作业
考虑A井恢复注水时间较短，地层注水还不稳定，故仅进行模拟测调，以验证测调工具的灵活性和可靠性。A井模拟测调结果见表1。
表 1 A井模拟测调结果Table 1. Simulation deployment results of Well A防砂层段 层位 配水器编号 配水器测调情况 第6防砂段 L50—L70 配6 将流量由490 m3/d调小到260 m3/d，再调大到480 m3/d，证明配水器测调正常 第5防砂段 L74—L80 配5 将流量由256 m3/d调小到188 m3/d，再调大到260 m3/d，证明配水器测调正常 第4防砂段 L82 配4 将流量由140 m3/d调小到60 m3/d，再调大到145 m3/d，证明配水器测调正常 第3防砂段 L84—L92 配3 转动配水器，调节流量不变，且电流由90 mA增大到118 mA，说明该层在此压力条件下不吸水，建议进行酸化处理 第2防砂段 L94—L96 配2 将流量由79 m3/d调小到45 m3/d，再调大到65 m3/d，证明配水器测调正常 第1防砂段 L100 配1 将流量由44 m3/d调小到15 m3/d，再调大到45 m3/d，证明配水器测调正常 现场作业中，6层模拟测调仅用时11 h，一体化配水器打开、关闭正常，大大提高了测调效率。
4.2 反洗井作业
由于A井注入水水质较差，注水3个月后地层吸水能力明显下降，判断井筒及近井地带出现了堵塞。为缓解地层堵塞问题，实施了反循环洗井作业，将井筒底部污染物携带至地面。
导通反洗井流程，环空注水排量16～25 m3/h，注水压力2.5～5.0 MPa，反洗井过程中控制注水排量，在保证地层无漏失或漏失较小的情况下，将反洗排量由小逐渐增大，待进出水水质一样时，停止反洗。洗井返出液的颜色如图5所示（从左向右按洗井作业时间的先后顺序排列）。
观察并分析图5可知，前期返出液较脏，含有大量的死油，静置后容器底部含有大量悬浮状泥质类物质；随着反洗水量增大，返出液逐渐变得清澈，说明反洗过程中携带出大量污染物。
该井于2018年9月17日后开始实施反洗作业，反洗作业前后的注水动态曲线如图6所示。
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图 6 A 井反洗作业前后的注水动态曲线Figure 6. Water injection dynamic curves before and after reverse washing in Well A从图6可以看出，反洗后该井的日注水量由之前的不足800 m3提高到了950 m3左右，注水量增加明显，说明反洗井工艺起到了解堵增注作用。
5. 结 论
1）常规分层注水工艺不具备反洗井功能，同时测调效率低，无法解决渤海油田注水开发中因注入水水质普遍较差易堵塞井筒与近井地带以及测调作业大量占用平台有限空间、影响其他作业等问题。
2）通过优化注水管柱，研制不动管柱反洗井封隔器，同时配套测调一体工具，形成了渤海油田可反洗测调一体分层注水工艺。
3）现场应用表明，渤海油田可反洗测调一体分注工艺测调效率高，平均单井调配工期仅需10 h，同时反洗井取得良好的降压增注效果，工艺优势明显，有助于推动该油田高效注水开发。
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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 
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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
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[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.
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