基于吸收分析的多属性反演技术及在油田开发中的应用

为了满足油藏开发阶段砂体内部油水识别的需求,提出了基于地层吸收分析的多属性反演流体识别技术。算法实现分三步:1利用基于广义S变换的瞬时子波吸收分析(GST_WEA)油气预测技术计算地震记录的高、低频吸收因子;2结合吸收因子和其它与流体相关的地震属性,以电阻率曲线为目标函数构建多元线性反演,利用多元逐步回归算法对包括吸收因子在内的多种地震属性进行流体敏感性排序;3进一步利用概率神经网络算法对优选的以吸收因子为主的地震属性进行多属性电阻率反演。通过模型分析验证了GST_WEA油气预测技术的抗噪性。实际应用效果表明吸收因子较其他地震属性具有更高的流体敏感性,通过多属性反演有效预测了砂体内部的含油优势区域。

The study on multi-attributes inversion based on absorption analysis and the application in oil development

For the purpose of the fluid recognition within sand in oil development, a multi-attributes inversion technique based on the seismic energy absorption analysis is proposed, and the algorithm implementation includes three steps: (1) using instantaneous wavelet absorption analysis (WEA) algorithm based on the generalized S-transform (GST) to make absorption analysis, and obtain the high/low frequency absorption factors Q; (2) combining Q with the other fluid related seismic attributes, we take the reflectivity logs as objective function to construct multi-variables linear inversion, and use stepwise regression algorithm to sort the fluid sensibility of seismic attributes including Q; (3) then using probabilistic neural network algorithm to make multi-attributes resistivity inversion for the choosing seismic attributes. By modeling analysis, the anti-noise of GST_WEA is verified. The application results show that the absorption has the higher fluid sensibility than the other seismic attributes. The proposed multi-attributes inversion technique can effectively indicate oil regions in sand.