滨里海盆地盐下碳酸盐岩储层地震逐级预测方法

Seismic Stepped Prediction Methods of Sub-salt Carbonate Reservoirs in Pre-Caspian Basin

滨里海盆地在盐下石炭系发育台地相碳酸盐岩,以裂缝—孔隙型储层为主,非均质性强,具有岩性复杂多样、储层物性和厚度变化快、单层厚度小的特征。针对这些难点,提出了随机地震反演和地质统计学协模拟实现储层的岩性和物性逐级预测。首先采用基于马尔科夫链—蒙特卡罗算法(MCMC)的随机地震反演获得高分辨率波阻抗和岩性反演结果,然后通过云变换方法分岩性建立波阻抗和孔隙度关系,并结合地质统计学协模拟对储层的孔隙度进行预测。反演结果纵向分辨率高,能识别2m以内的薄储层,同时井间横向变化合理,真实地反映了碳酸盐岩储层变化特征。该方法能解决类似非均质性薄储层预测的地质问题。

In the sub-sail carboniferous of Pre-Caspian Basin widely develops platform carbonate reservoirs, which belong to fractured-porous type with strong heterogeneity. The reservoirs are featured by complex and various lithology,great lateral variable physical property,and thin layer. The favorable reservoir prediclion becomes an exploration problem. The paper proposes stochastic seismic inversion and geostatistics association simulation methods to predict lithology and physical property gradually. Stochastic seismic in- version based on Markov chain Monte Carlo algorithm （MCMC） can obtain high-resolution impedance and lithology data. It firstly builds the prior probability of stochastic variable by the geostalistical analysis of well data,and then acquires the posterior probability with Bayes formula under the constraint of seismic data, finally resample the data according to the posterrior probability by MCMC. Based on the previous predicted data,it builds the relationship between impedance and physical property parameter by cloud transform,and predicts the physical property parameter by geostatistical simulation. The inversion result has high vertical resolution and the identified minimum thickness of the reservoir layer is below 2 meters. The lateral variation truly reflects the characteristics of carbonate reservoir. The above methods can solve simi- lar strong heterogeneous thin reservoir prediction problems.