深水浅层松散沉积物水合物赋存状态识别及饱和度计算

Jun Zhao , Chao Zheng , Jianxiang Pei , Di Tang , Jiang Jia
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引用次数: 0

摘要

中国近海地区拥有巨大的深水和浅层天然气水合物储量。然而,由于深水和浅层地质松散,缺乏致密封层,天然气水合物储层非均质性强,对天然气水合物赋生状态的识别仍然具有挑战性,极大地阻碍了天然气水合物饱和度的准确预测。根据深水和浅层天然气水合物的声电响应特征,采用电阻率与纵波速度图交会法识别天然气水合物赋存状态。天然气水合物储层孔隙体积的计算采用经泥浆含量校正的密度公式。采用泥校正电阻率法、等效介质法和联合反演法对琼东南盆地YL靶区天然气水合物饱和度进行了预测,找到了声电资料组合误差最小的方法。结果表明,在QDN盆地YL靶区,联合反演方法预测值与氯离子浓度法实测值最接近,预测误差在0.09% ~ 14.89%之间,平均误差为6.85%。综上所述,在确定水合物赋存状态的基础上,选择合适的声电联合反演饱和度计算模型,可以显著提高水合物饱和度预测的精度。该方法为深水和浅层沉积物中水合物饱和度的计算提供了一种可行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hydrate occurrence identification of shallow loose sediments in deep water and its saturation calculation
The Chinese offshore area holds vast reserves of deepwater and shallow gas hydrates. However, due to the geological looseness of deepwater and shallow layers, the absence of tight sealing layers, and the high heterogeneity of gas hydrate reservoirs, identifying the occurrence state of gas hydrates remains challenging, greatly impeding the accurate prediction of gas hydrate saturation. Based on the acoustic-electric response characteristics of deepwater and shallow gas hydrates, this study employs the intersection method of resistivity and longitudinal wave velocity diagrams to identify the occurrence state of gas hydrates. The pore volume of gas hydrate reservoirs is calculated using a density formula corrected for mud content. Gas hydrate saturation in the YL target area of the Qiongdongnan (QDN) Basin is predicted using three methods: the mud-corrected resistivity method, the equivalent medium method, and the joint inversion method, finding the minimum combined error of acoustic and electric data. The results indicate that the predicted values using the joint inversion method in the YL target area of the QDN Basin are closest to the measured values obtained from the chloride ion concentration method, with prediction errors ranging from 0.09 % to 14.89 % and an average error of 6.85 %. These findings suggest that selecting an appropriate acoustic-electric joint inversion saturation calculation model, based on the determination of hydrate occurrence states, can significantly improve the accuracy of hydrate saturation prediction. This approach provides a realiable method for calculating hydrate saturation in the deepwater and shallow sediments.
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