基于稀疏贝叶斯学习的薄储层预测方法及应用

薄储层是近年来我国油气勘探的重点目标之一,提高地下薄储层识别能力对油气勘探具有重要意义。对于薄储层预测,由于地震分辨率及邻层干涉等因素的制约,基于反射地震数据直接探测地下薄储层的难度较大。为此,采用一种新的稀疏贝叶斯学习理论开展地震反射系数反演,并在获取地层反射系数的基础上计算地层相对波阻抗信息,通过设计线性FIR滤波器滤除地层相对波阻抗计算过程中的低频累积误差,进而开展薄储层高精度预测。实际资料应用表明:新的基于稀疏贝叶斯学习理论的地震反射系数反演方法可大幅度提高地层反射系数的计算精度,为获取高精度地层相对波阻抗奠定了基础;设计的线性FIR滤波器能够有效拟制地层相对波阻抗中的低频累积误差,提高了薄储层识别精度。与传统地震振幅属性相比,本次研究获取的地层相对波阻抗信息能更精确地表征薄储层平面形态展布特征,并能有效提高薄储层勘探的成功率。

A new method for thin reservoir identification based on sparse Bayesian learning and its application

In recent years,thin reservoir is one of the main targets of oil and gas exploration in China.To enhance the ability of thin reservoir identification can be significant for detecting the residual hydrocarbon resources.Due to the limitation of seismic resolution and the interference of seismic reflection among neighboring thin layers,it is a tricky task to directly evaluate thin reservoir based on the seismic reflection data.Therefore,a new method of seismic reflectivity inversion based on sparse Bayesian learning was adopted,and then the relative acoustic impedance of underground strata was estimated by the inverted seismic reflectivity.A linear FIR filtering was also introduced to eliminate the low-frequency accumulative errors which were generated in the processing of relative impedance calculation.Thin reservoir can be finally identified based on the filtered relative impedance with higher confidence.Field application shows that the accuracy of seismic reflectivity can be improved significantly by seismic reflectivity inversion based on sparse Bayesian learning,laying a solid foundation for a better estimation of relative impedance of underground formation.The designed linear FIR filter can effectively suppress the low-frequency accumulative errors in the estimated relative impedance,improving the accuracy of thin reservoir detection.Comparing with the traditional seismic amplitude,the relative impedance estimated by the proposed method can reveal the lateral distribution of thin reservoir more accurately,and finally improves the success rate of thin reservoir exploration.