南海西部深水区陵水17-2气田气藏描述地球物理关键技术

发布时间：2018-05-09 18:44

本文选题：深水气田 + 气藏描述　；参考：《吉林大学》2016年硕士论文

【摘要】：近年位于南海西部琼东南盆地的深水区天然气勘探取得重大突破,发现了我国第一个自营勘探的深水高产大气田—陵水17-2气田。但在深水气田的气藏描述方面,仍然面临砂体沉积模式复杂,砂体刻画困难、地层横向速度变化大,构造落实精度低及储层与含气性预测结果不确定性大等问题。为了提高深水气藏的描述精度,精确评价深水气藏的储量规模,本论文以陵水17-2气田储量研究项目为依托,针对深水区气藏描述中的三项关键技术进行了研究,解决了深水区气藏描述中的砂体及水道刻画问题、时深转换问题、储层及含气性预测问题。针对深水区多砂体沉积、多水道侵蚀,沉积模式复杂,砂体刻画难的问题,首先以区域沉积规律为指导,结合地震相干切片、频率融合切片解释等技术开展大尺度下的峡谷及水道的刻画;在此基础上,进行小尺度下的砂体描述,利用宏观规律指导微观解释,峡谷及水道的空间形态约束各砂体的解释范围,利用全三维可视化地震解释技术开展砂体精细刻画研究,精确落实了深水区砂体及水道的纵横向展布。针对深水区横向速度异常变化剧烈、时深转换困难的难题,对比业界常用的各种时深转换方法,在落实深水区地层速度规律的基础上,分析了各种时深转换方法在深水区的适用性,优选出速度体建模的方法做为适用于深水区的时深转换方法。以速度体建模方法为基础,在速度体的优化方法、速度建模的细节方面开展了深入研究,在速度体优化方面,对原始速度品质分析的基础上,利用中值滤波算法对速度体的品质进行优化,优选出最优的滤波窗口和滤波次数;在速度建模方面,开展了建模层位的优选研究,根据该区的构造沉积特征,优选出最优的模型控制层位。最后利用高精度建模得到的速度模型完成了各砂体的时深转换,还原了地下砂体的真实构造,利用交叉井验证的方法,分析了该方法预测的可靠性。针对深水区钻井数量少,井控不足引起的储层预测及含气性预测困难问题,从已钻井岩石物理特征分析入手,开展了以叠后叠前反演为基础的储层预测研究,结合平面上的沉积相认识及钻井统计规律,利用储层反演成果对砂体的横向展布特征及储层物性的空间展布规律进行预测;在落实储层的基础上,开展了基于振幅分析的半定量到定量含气预测、以谱分解技术为基础的含气性预测研究,多角度预测结果相结合预测砂体的含气边界,同时与构造描述的含气范围进行交叉验证,精确落实了各砂体的含气范围。经过本次研究,解决了研究区的砂体精细刻画、时深转换方法落实、储层及含气性预测等方面的诸多难点。首次将基于中值滤波的速度体优化技术及井控层控的高精度速度建模技术应用于深水区时深转换;首次在深水区开展振幅定量化、谱分解、属性分析相结合的含气性预测研究,精确预测了浊积砂体的储层及气层展布。研究成果为中国第一个自营深水大气田陵水17-2气田的储量评价提供了可靠的数据基础,同时为正在进行的中国海油深水天然气勘探提供了有效的技术借鉴。
[Abstract]:In recent years, the exploration of natural gas in the deep water area of the Southeast qiongnan basin of the South China Sea has made a major breakthrough. The first self exploration of the deep water field, Lingshui 17-2 gas field, is found in our country. However, in the description of the gas reservoir in the deep water gas field, the sand body deposition model is still complicated, the portrayed sand body is difficult, the lateral velocity of the formation is changed greatly and the structure falls. In order to improve the accuracy of the description of the deep water gas reservoir and to accurately evaluate the reserves of the deep water gas reservoir, this paper, based on the three key key technology in the description of the deep water gas reservoir, has solved the gas reservoir in the deep water area in order to improve the accuracy of the description of the deep water gas reservoir and accurately evaluate the reserves of the deep water gas reservoir. The paper, based on the Lingshui 17-2 gas field reserve research project, has studied the deep water reservoir description. The description of sand body and waterway portrayed, time depth conversion, reservoir and gas bearing prediction. In view of the problems of multi sand body deposition, multi channel erosion, complex sedimentary model and difficult sand body characterization in deep water area, first of all, it is guided by regional depositional law, combined with seismic coherent slice and frequency fusion section interpretation to carry out large scale. On the basis of the portrayal of Canyon and waterway, the description of sand body under small scale is carried out, micro interpretation is guided by macro law, the spatial form of Canyon and waterway constrains the interpretation range of each sand body, and the fine depiction of sand body is carried out by the full three-dimensional visual seismic interpretation technology, and the vertical and horizontal direction of the sand body and the waterway in the deep water area is accurately implemented. In order to solve the difficult problem of transversal velocity anomaly in deep water area and difficult time depth conversion, a variety of time depth conversion methods commonly used in the industry are compared. On the basis of implementing the law of formation velocity in deep water, the applicability of various time depth conversion methods in the deep water area is analyzed, and the method of velocity body modeling is selected as the time to apply to the deep water area. Deep conversion method. Based on the speed body modeling method, the optimization method of velocity body and the details of speed modeling are studied. On the basis of speed body optimization, the quality of speed body is optimized by median filtering algorithm on the basis of the analysis of the original speed quality, and the optimal filter window and filter times are optimized. In the field of velocity modeling, the optimization study of the modeling layer is carried out. According to the characteristics of the tectonic deposition in the area, the optimal model control layer is selected. Finally, the time depth conversion of each sand body is completed by the speed model obtained by high precision modeling, the real structure of the underground sand body is reduced, and the method of cross well verification is used to analyze the method. In view of the low number of drilling in deep water and the difficult problem of reservoir prediction and gas bearing prediction caused by insufficient well control, starting with the analysis of the physical characteristics of drilling rock, the reservoir prediction research based on pre stack inversion is carried out, and the reservoir inversion results are used in combination with the sedimentary recognition and drilling Statistics Law on the plane. The transverse distribution characteristics of sand body and the spatial distribution law of reservoir physical property are predicted. On the basis of reservoir implementation, the gas bearing prediction research based on the semi quantitative to quantitative gas prediction based on amplitude analysis is carried out based on the spectral decomposition technique, and the multi angle prediction results are combined to predict the gas bearing boundary of the sand body, and the structure is described with the structure. The gas range of the sand body is verified by cross validation, and the gas range of each sand body is carried out accurately. After this study, the fine depiction of sand body, the implementation of time depth conversion method, the reservoir and gas bearing prediction are solved. The technology of velocity body optimization based on median filtering and the high precision speed modeling technique of well controlled layer control are first applied. The technique is applied to the deep depth conversion in the deep water area; the reservoir and reservoir distribution of the turbidite sand body are accurately predicted by the gas prediction research combined with amplitude quantification, spectral decomposition and attribute analysis in the deep water area for the first time. The results provide a reliable data basis for the reserves evaluation of Lingshui 17-2 gas field in the first self operated deepwater atmosphere field. This provides effective technical reference for the ongoing exploration of deep sea natural gas in China Sea oil.

【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：P618.13

【相似文献】