摘要
在W地区,储层孔隙度预测技术是将支持向量机方法与分频法相结合构成一种新的地震非线性预测技术。支持向量机是一种基于统计理论的新型机器学习算法,它是建立输入与输出的一个隐形的映射关系,在我们的问题中,应用测井数据作为支持向量机的输入向量,通过支持向量机优选参数,对实际样本数据构造核函数,实现全局最优解。分频法采用短时快速傅利叶变换(SFFT)将地震数据进行时频转换,形成高分辨的分频数据体,作为独立的输入数据体,以提高储层孔隙度反演的分辨率和精度。因此,构成的这种联合预测技术,充分发挥了支持向量机和分频法的优势,这种全新的储层孔隙度预测技术适应储层地质参数高维非线性特点。依据建立的储层孔隙孔隙度预测技术,利用W地区实际地震数据形成的分频数据体和测井数据,对朱海组储层孔隙度进行了预测,获得了高分辨率和高精度储层孔隙度剖面和储层孔隙度数据体。在W地区,预测的朱海组储层孔隙度与测井孔隙度具有良好的一致性,其相关系数值为(0.816～0.945),平均值为0.882,因此,预测的储层孔隙度具有较高的可信度和准确率。在W地区成功应用表明,其方法技术具有通用性和较好的推广性,它的适用面广,预测效果好,易于实现,它将油气储层孔隙度预测推向一个新的发展水平。
In the W district, a new nonlinear seismic prediction method combined with the SVM method and the frequency division method are used in reservoir porosity prediction. As a new ma- chine learning algorithm based on statistical theory,the SVM built an invisible mapping relation-ship between input and output. In fact,the log data are applied as the input vector of the SVM. Then,optimized parameters are picked by the SVM, the kernel function for sampled data is con-structed, and the global optimum is achieved. While, the frequency division method uses the Short-Time Fast Fourier Transform （SFFT） to seismic data to recognize frequency transformation,from which frequency divides data volume with high resolution can be got and input as individual data volume to improve the solution and accuracy of reservoir porosity inversion. The combined predic-tion method is of the advantage of two methods mentioned above, and it works well in the reser-voir of high nonlinear features. In this paper,the reservoir porosity prediction technique is applied to the frequency divided data and the log data, based on actual seismic data of the W district,to predict the porosity of Zhuhai Formation,and obtain its reservoir porosity profile and data volume of high solution and accuracy. As result, the prediction porosity of Zhuhai Formation was consist-ent with the log porosity,with their correlation coefficient of 0. 816-0. 945 ,and with average val- ue of 0. 882,demonstrating the high reliability and accuracy of this prediction method. The suc-cessful application of this technique in the W district proves that it is of great generality and can be extended to more cases with good prediction result.
出处
《矿物岩石》
CAS
CSCD
北大核心
2012年第3期110-115,共6页
Mineralogy and Petrology
基金
中国海洋石油能源发展科技项目(项目号:HFKJ-CJK1107)
关键词
储层孔隙度
测井孔隙度
支持向量机
分频法
地震预测技术
reservoir porosity
log porosity
SVM
frequency division method
seismic predictiontechnique
