柴达木盆地西部古近系咸化湖盆烃源岩总有机碳含量预测

doi:10.3969/j.issn.1006-6535.2021.01.010

特种油气藏 ›› 2021, Vol. 28 ›› Issue (1): 74-80.DOI: 10.3969/j.issn.1006-6535.2021.01.010

柴达木盆地西部古近系咸化湖盆烃源岩总有机碳含量预测

太万雪^1,2, 刘成林^1,2, 田继先³, 冯德浩^1,2, 曾旭³, 李培^1,2, 孔骅³

1.油气资源与探测国家重点实验室,北京 102249;
2.中国石油大学北京,北京 102249;
3.中国石油勘探开发研究院,河北廊坊 065007

收稿日期:2020-05-27 修回日期:2020-09-01 出版日期:2021-02-25 发布日期:2021-04-27
通讯作者: 刘成林(1970—),男,教授,1994年毕业于石油大学(北京)综合勘探专业,2004年毕业于该校矿物学、岩石学与矿床学专业,获博士学位,现主要从事油气地球化学与资源评价、非常规油气地质研究和教学工作。
作者简介:太万雪(1996—),女,2018年毕业于东北石油大学地质学专业,现为中国石油大学(北京)地质资源与地质工程专业在读硕士研究生,主要从事石油地质勘探与资源评价工作。
基金资助:
国家自然科学基金面上项目“碳沥青与钒矿物伴生机理研究”(41272099)、“咸化湖盆条件下盐类对地层超压的作用机制研究”(41872127)

Prediction of Total Organic Carbon Content of Source Rocks in Paleogene Salinized Lake Basin in Western Qaidam Basin

Tai Wanxue^1,2, Liu Chenglin^1,2, Tian Jixian³, Feng Dehao^1,2, Zeng Xu³, Li Pei^1,2, Kong Hua³

1. State Key Laboratory of Oil and Gas Resources and Exploration, Beijing 102249, China;
2. China University of Petroleum (Beijing, Beijing 102249, China;
3. Langfang Branch of PetroChina Research Institute of Petroleum Exploration and Development, Langfang, Hebei 065007, China

Received:2020-05-27 Revised:2020-09-01 Online:2021-02-25 Published:2021-04-27

摘要/Abstract

摘要： 针对咸化环境影响柴达木盆地西部地区烃源岩有机碳预测问题,根据盐度变化采取了优化的ΔlgR法、多元回归法、以及BR-BP神经网络方法进行有机碳含量的预测,探讨3种模型对有机碳预测结果的差异。研究结果表明:多元回归模型预测效果较差,优化后ΔlgR模型准确性较多元回归模型有所提高,但普适性较差,BR-BP神经网络模型在高咸化和中低咸化地区表现有差异,但预测效果均较好。因此,在中—低盐度地区宜采用神经网络模型预测,在高盐度地区,应合理调整神经网络模型参数,搭配ΔlgR模型综合计算。研究成果建立的模型可以提高烃源岩识别的精度,指导盆地内精确的烃源岩评价。

关键词: 有机碳预测, 咸化环境, 神经网络, 模型优选, 预测模型, 测井参数, 柴西地区

Abstract: In view of the influence of salinized environment on organic carbon prediction of source rocks in western Qaidam Basin, the optimized ΔlgR method, multiple regression method and BR-BP neural network method were used to simulate organic carbon content according to salinity changes, and the differences of organic carbon prediction results of the three models were discussed. The results show that: The prediction effect of multiple regression model is general.The accuracy of the optimized ΔlgR model is higher than that of the multiple regression model, but its universality is general.The BR-BP neural network model has different performance in high salinity and medium-low salinity areas, but the prediction effect is better. Therefore, we propose applying the neural network model to predict in medium-low salinity areas, and conducting comprehensive calculation by reasonably adjusting the parameters of the neural network model and combining with the ΔlgR model in high salinity areas. The research results can improve the accuracy of source rock identification and guide accurate source rock evaluation in the basin.

Key words: organic carbon prediction, salinized environment, neural network, model optimization, prediction model, well logging parameter, western Qaidam Basin

中图分类号:

TE133

太万雪, 刘成林, 田继先, 冯德浩, 曾旭, 李培, 孔骅. 柴达木盆地西部古近系咸化湖盆烃源岩总有机碳含量预测[J]. 特种油气藏, 2021, 28(1): 74-80.

Tai Wanxue, Liu Chenglin, Tian Jixian, Feng Dehao, Zeng Xu, Li Pei, Kong Hua. Prediction of Total Organic Carbon Content of Source Rocks in Paleogene Salinized Lake Basin in Western Qaidam Basin[J]. Special Oil & Gas Reservoirs, 2021, 28(1): 74-80.

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柴达木盆地西部古近系咸化湖盆烃源岩总有机碳含量预测

Prediction of Total Organic Carbon Content of Source Rocks in Paleogene Salinized Lake Basin in Western Qaidam Basin

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