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渤海湾盆地渤中凹陷西南部深层烃源岩有机相预测
引用本文:王祥,马劲风,王飞龙,王震亮,陈容涛,闫昕宇.渤海湾盆地渤中凹陷西南部深层烃源岩有机相预测[J].石油实验地质,2022,44(6):1070-1080.
作者姓名:王祥  马劲风  王飞龙  王震亮  陈容涛  闫昕宇
作者单位:1. 西北大学 地质学系, 西安 710069;
基金项目:中海石油(中国)有限公司天津分公司课题“渤中凹陷深层成烃成藏机理与规律研究”(CCL2018TJTZDST0579)资助。
摘    要:针对渤海湾盆地渤中凹陷西南部烃源岩层系多、非均质性较强等特点,开展了烃源岩干酪根显微组分、元素分析、岩石热解、气相色谱等地球化学分析测试,在沉积相的约束下对比了ΔlogR系列方法、多元回归法、BP神经网络法预测有机质丰度参数的优劣,优选BP神经网络法进行烃源岩总有机碳含量、裂解烃含量的单井预测,进而计算得到氢指数预测曲线,结合总有机碳含量进行有机相分析,定量刻画各层系烃源岩的有机相,分析烃源岩的优劣和展布特征。结果表明,BP神经网络法的预测精度相对较高,东营组二段下亚段烃源岩主要为Ⅱ2型干酪根,氢指数为125~400 mg/g,总有机碳含量基本小于3%,其有机相主要为BC相、C相和CD相;东营组三段烃源岩主要为Ⅱ1—Ⅱ2型干酪根,沙河街组沙一二段、沙三段烃源岩主要为Ⅰ—Ⅱ1型干酪根,这三套烃源岩的氢指数为250~650 mg/g,总有机碳含量为3%左右或大于3%,其有机相主要为B相、BC相和C相。沙河街组优质烃源岩主要发育于研究区中南部,是下一步勘探的重点区域。 

关 键 词:总有机碳含量    ΔlogR方法    有机相    烃源岩    BP神经网络    渤中凹陷    渤海湾盆地
收稿时间:2021-07-29

Prediction of organic facies of deep source rocks in southwestern part of Bozhong Sag,Bohai Bay Basin
Affiliation:1. Department of Geology, Northwest University, Xi'an, Shaanxi 710069, China;2. National & Local Joint Engineering Research Center of Carbon Capture and Storage Technology, Xi'an, Shaanxi 710069, China;3. State Key Laboratory of Continental Dynamics, Xi'an, Shaanxi 710069, China;4. Exploration and Development Research Institute, Tianjin Branch of CNOOC, Tianjin 300452, China
Abstract:Considering the characteristics of abundant source rock formations and heterogeneity in the southwestern part of the Bozhong Sag, Bohai Bay Basin, based on the geochemical and petrographic analysis of source rocks, the methods of ΔlogR series, multiple regression and BP neural network are applied to predict the organic matter abundance under the constraints of sedimentary facies.The BP neural network method is preferably used to predict the total organic carbon content and cracked hydrocarbon content of source rocks in a single well, and then the hydrogen index prediction curve is calculated.Combined with total organic carbon content,the organic phase of each source rock layer is quantitatively described, and the advantages and disadvantages and distribution characteristics of source rocks are analyzed. Results show that the prediction accuracy of BP neural network method is relatively higher. The source rocks in the lower section of the second member of Dongying Formation are featured by kerogen of type Ⅱ2, hydrocarbon index of 125-400 mg/g, TOC content basically less than 3%, and organic facies of BC, C and CD types. The source rocks in the third member of Dongying Formation are featured by kerogen of typesⅡ1 to Ⅱ2. The source rocks in the first, second and third members of Shahejie Formation are featured by kerogen of types Ⅰ to Ⅱ1, TOC content about 3% or higher than 3%, and organic facies of B, BC and C types.The high-quality source rocks of the Shahejie Formation are mainly developed in the central and southern parts of the study area, and are the key areas for further exploration. 
Keywords:
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