摘要
在分析古龙页岩油储层及测井响应特征基础上,系统总结了储层“四性”关系及页岩油甜点测井评价技术研究现状,指出了测井评价面临的技术挑战。通过与准噶尔盆地吉木萨尔凹陷、渤海湾盆地沧东凹陷等国内其他页岩油储层典型特征对比,给出了在大数据人工智能驱动下解决测井评价瓶颈技术的相关对策,明确了古龙页岩油储层测井评价的关键是优势岩性岩相及其空间叠置关系识别与表征。结合大数据人工智能技术,实现缺失曲线、坏井眼等影响因素下的曲线智能重构;充分发挥大数据在数据信息深度挖掘方面的独特优势,开展单井—多井—区域储层对比、类比及延展性预测,进而结合“四性”关键参数实现优势甜点区带评价,研究形成基于大数据分析的古龙页岩油储层测井解释评价技术,为古龙页岩油下一步勘探部署、储量提交和开发方案编制等提供测井关键技术支撑。
On the basis of analyzing the characteristics of Gulong shale oil reservoirs and logging response,the research status of the reservoir“four-property”relationship and logging evaluation technology for the shale-oil sweet spot were systematically summarized,the technical challenges faced by the logging evaluation were pointed out.With the help of the comparisons of the typical characteristics of other shale oil reservoirs in China,such as Jimsar Sag in Junggar Basin,Cangdong Sag in Bohai Bay Basin and so on,the relative countermeasures of the logging-evaluation bottleneck technology driven by the big-data artificial intelligence were presented.It is clear that the key of the logging evaluation for Gulong shale oil reservoirs is the identification and characterization of the dominant lithology-lithofacies and their spatial superposition relationships.Combined with big-data artificial intelligence technology,the curve intelligent reconstruction under the influencing factors such as the missed curve,damaged well bore and so forth were realized;the unique advantage of the big data in the data-information deep mining was fully shown,the correlation,analogy and ductility predictions for the individual well-multiwell-regional reservoirs were carried out,and then combined with the“four-property”key parameters,the evaluation of the dominant sweet-spot zone and belt was realized,thus the well-logging interpretation and evaluation technologies for Gulong shale oil reservoirs based on the big data analysis were developed,which have provided the key technical support in the well logging for the next exploration deployment,reserves submission and development plan compilation of Gulong shale oil.
作者
李宁
闫伟林
武宏亮
郑建东
冯周
张兆谦
王克文
王敬岩
LI Ning;YAN Weilin;WU Hongliang;ZHENG Jiandong;FENG Zhou;ZHANG Zhaoqian;WANG Kewen;WANG Jingyan(Research Institute of Petroleum Exploration&Development,PetroChina,Beijing 100083,China;Exploration and Development Research Institute of Daqing Oilfield Co Ltd,Daqing 163712,China;Heilongjiang Provincial Key Laboratory of Shale Oil&Tight Oil Accumulation,Daqing 163712,China)
出处
《大庆石油地质与开发》
CAS
CSCD
北大核心
2020年第3期117-128,共12页
Petroleum Geology & Oilfield Development in Daqing
基金
国家科技重大专项“松辽盆地北部致密油资源潜力、甜点区预测与关键技术应用”(2016ZX05046)。
关键词
古龙页岩油
四性关系
测井评价
大数据人工智能
岩性岩相
甜点评价
Gulong shale oil
four-property relationship
logging evaluation
big-data artificial intelligence
lithology and lithofacies
sweet-spot evaluation