Multi-attribute optimization analysis for sandstone porosity prediction
WANG Zhi-guo1, YIn Cheng1, LEI Xiao-lan2, LI Chang-chun3, FAN Ting-en4
1 School of Resources and Environment Engineering,Southwest Petroleum University,Chengdu,Sichuan 610500,China;
2 The Second Gas Production Plant,Changqing Oilfield,CNPC,Xi'an,Shaanxi 710200,China;
3 CNOOC Energy Development and Services-Supervision Technology Company,Tianjin 300452,China;
4 CNOOC Research Center,Beijing 10027,China
Abstract:When we deal with a large number of seismic attributes,we have to choose some of them for optimization to improve reservoir prediction.In this work,we introduce a set of mathematical expressions for multi-attribute optimization analysis based on compatibility and redundancy,independence and non-related of multi-attribute.We combine KL transform highlighting independence,RS optimization highlighting compatibility,and SDC optimization highlighting relevance into 8 series optimization model for comparison.Then we input these 8 groups of optimized attributes in RBFNN to extrapolate porosity of sandstones.Finally,we apply our workflow on the field data from Bohai Bay,China.This field example shows that RS-KL-RBFNN is the most effective workflow for reliable porosity measurements and the relative error of 8 porosity estimated by 8 processes are acceptable for reservoir characterization.