叠合概率法在碳酸盐岩储层评价中的应用——以辽河坳陷西部凹陷高升地区沙四段为例
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摘要
辽河坳陷西部凹陷高升地区沙四段碳酸盐岩储层的形成和发育主要受沉积相、埋藏溶蚀作用及构造破裂作用的共同控制,储层在微观和宏观上具有强烈的非均质性,储层影响因素较多且孔渗关系复杂。为了避免仅靠某一单因素、少数有限的钻井资料及物性测试资料进行区域性碳酸盐岩储层评价容易出现偏差的问题,运用特征选择中的Relief F算法对碳酸盐岩厚度、粒屑灰(云)岩厚度、孔隙度、渗透率、脆性指数及裂缝密度6项参数进行权重分析,并确定各参数权重值,然后使用叠合概率法将不同特征平面图加权叠合形成一张多因素概率图,通过划分概率区间最终形成储层评价图。同时综合考虑西部凹陷高升地区的构造特征及沉积相特征,对研究区沙四段碳酸盐岩储层进行研究评价,评价结果与研究区已勘探油气分布具有较好的一致性,并预测出有利勘探区带。
The formation and development of carbonate reservoirs in 4~(th) member of Shahejie Formation(Es_4) in Gaosheng area, western sag in Liaohe depression, are mainly controlled by sedimentary facies, burial dissolution and tectonic fractures. Whether microscopically or macroscopically, the reservoirs are strongly heterogeneous and controlled by many factors, and the porosity and permeability are very complex. In order to avoid the deviation caused by single factors, and less drilling and logging data when evaluating regional carbonate reservoirs, the Relief F algorithm for feature selection was used to analyze the thickness, porosity, permeability, brittleness index and fracture density of the carbonate rock and the grained limestone(dolomite), and their weights; then the feature plans were overlapped to create a multi-factor probability map; and finally a reservoir evaluation map was built by dividing the probability. In addition, considering the structural and sedimentary facies characteristics in research area, the carbonate reservoirs in Es_4 were investigated and evaluated. The evaluation results are in good agreement with the results from field exploration. Based on the results, favorable exploration zones were predicted.
The formation and development of carbonate reservoirs in 4~(th) member of Shahejie Formation(Es_4) in Gaosheng area, western sag in Liaohe depression, are mainly controlled by sedimentary facies, burial dissolution and tectonic fractures. Whether microscopically or macroscopically, the reservoirs are strongly heterogeneous and controlled by many factors, and the porosity and permeability are very complex. In order to avoid the deviation caused by single factors, and less drilling and logging data when evaluating regional carbonate reservoirs, the Relief F algorithm for feature selection was used to analyze the thickness, porosity, permeability, brittleness index and fracture density of the carbonate rock and the grained limestone(dolomite), and their weights; then the feature plans were overlapped to create a multi-factor probability map; and finally a reservoir evaluation map was built by dividing the probability. In addition, considering the structural and sedimentary facies characteristics in research area, the carbonate reservoirs in Es_4 were investigated and evaluated. The evaluation results are in good agreement with the results from field exploration. Based on the results, favorable exploration zones were predicted.
引文
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[18]魏永佩,陈会鑫.人工神经网络系统在储集层评价中的应用[J].西安工程学院学报,1998,20(2):33-35.Wei Yongpei,Chen Huixin.The application of artificial neural network in reservoir rock evaluation[J].Journal of Earth Sciences and Environment,1998,20(2):33-35.
[19]黄渊,廖明光,李斌,胡晓蝶,胡雯雯.数据挖掘技术在碳酸盐岩储层评价中的应用[J].特种油气藏,2014,21(5):37-42.Huang Yuan,Liao Mingguang,Li Bin,Hu Xiaodie,Hu Wenwen.Application of data mining technique in the evaluation of carbonate reservoir[J].Special Oil&Gas Reservoirs,2014,21(5):37-42.
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