叠后多属性分析在哈山西石炭系火山岩裂缝预测中的应用研究

Application of multiple post-stack seismic attributes in the prediction of carboniferous fracture in west Hashan

裂缝油气藏在油气勘探开发中占有重要地位,准确地预测储层中的裂缝发育程度等参数是勘探开发中的难点.本文采用叠后多属性分析技术对哈山西地区的石炭系火山岩裂缝密度进行了定量预测:在储层特征分析的基础上,首先提取了对裂缝发育反映较好的多种地震属性,包括相干体、倾角方位角、曲率、蚂蚁体、弧长、瞬时频率、均方根振幅、反射强度、道微分、吸收衰减属性,并对提取的属性进行了敏感性分析.在此基础上,利用BP神经网络的方法,对裂缝密度进行了定量预测,预测结果与工区钻井资料吻合,为下步的勘探开发提供了依据.

Fractured reservoirs in oil and gas exploration and development plays an important role, a more accurate prediction of reservoir parameters such as fracture density is one of the difficulties in exploration.This paper uses multiple post-stack attributes analysis technologies to quantitatively predict carboniferous volcanic fracture density in west Hashan：firstly it extracts a variety of seismic attributes those are better reflecting the fracture,including coherence,dip-azimuth,curvature,ants body,arc length,instantaneous frequency,RMS amplitude reflection strength,differential,absorption attenuation attributes,and then analysis these attributes’ the sensitivity to the fracture.On this basis,with use of BP neural network method,it makes a quantitative prediction fracture density,and predictions coincide with the drilling data in the work area,provides a basis to the next step to exploration and development.