【摘要】 南堡油田中、浅层火成岩发育,分布特征复杂,岩相、厚度变化快,影响了油气成藏规模,同时也制约了对油藏的整体认识。钻井揭示,火成岩以玄武岩和凝灰岩为主。在地震剖面上,玄武岩与馆陶组底界砂砾岩的特征接近;在常规波阻抗反演剖面上,凝灰岩与沉积岩的波阻抗值范围相近。为此,首先通过测井数据综合分析,利用录井岩性与测井敏感曲线构建出能反映岩性差异的伪岩性曲线;然后利用叠前AVO处理,得到梯度(G)、截距(P)和泊松比(σ)等属性数据体,并提取了振幅、频率、相位等多种地震属性;最后以伪岩性曲线为目标曲线,优选敏感性高的一组地震属性作为反演参数,利用概率神经网络反演技术对南堡油田火成岩进行了识别和描述,结果与地质认识基本相符。更多还原

【Abstract】 Igneous rock develops in shallow and middle layers in Nanpu oilfield.The igneous rock is characterized by complex distribution and fast alternation of lithofacies and thickness,which affects the hydrocarbon accumulation scale and its recognition as a whole. Drilling formation indicates that the igneous rock is mainly of basalt and tuff.On seismic profiles, the characteristics of basalt are simi- lar to that of glutinite at the bottom of Guantao formation.In con- ventional wave impedance inversion profile,the impedance value of tuff is similar to that of sedimentary rock.Therefore,the integrat- ed analysis of logging data was carried out firstly and the pseudo-li- thology curve was established through logging lithology and sensi- tive curve,which can reflect the difference of lithology.Then,the pre-stack AVO processing was used to obtain attribute data vol- ume,including gradient(G),intercept(P).and Poisson ratio(σ) to extract seismic attributes including amplitude,frequency and phase.Finally,taking the pseudo-lithology curve as the target one,the seismic attributes with high sensitivity was optimized as inversion parameters,PNN inversion technology based on neural network was used to identify and describe the igneous rock in Nan- pu oilfield.The result coincides with the geological recognitions.更多还原