In this study a tight carbonate gas reservoir of early Eocene (S1 formation) is studied for litho-facies estimation and probabilistic estimation of reservoir properties prediction using quantitative geophysical approach from a mature gas field in the Middle Indus Basin, onshore Pakistan. Quantitative seismic reservoir characterization approach relied on well based litho-facies re-classification, Amplitude Variation with Offset (AVO) attributes analysis and Pre-Stack simultaneous inversion attributes constrained with customized well-log and seismic data (gathers) conditioning. Three main litho-facies (hydrocarbon bearing limestone, tight limestone and shale) are classified estimated based on the precise analysis of well data using petrophysical properties. AVO attributes (intercept and gradient) conveniently inspection for amplitude behavior (reflection coefficients) of the possible AVO (class I), fluids and lithology characteristics. Probable litho-facies (tight limestone and shale) are estimated using well based litho-facies classification and inverted seismic attributes (p-impedance and density) from pre-stack simultaneous inversion in a Bayesian framework. Additionally, petrophysical properties (clay volume and porosity) are derived from probabilistic neural network approach using well logs and pre-stack inverted attributes (pimpedance and density) constrained with sample-based seismic attributes (instantaneous, windowed frequency, filters, derivatives, integrated and time).

中文翻译：

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定量地球物理方法表征致密碳酸盐岩储层的特征和概率估计：以巴基斯坦中印度盆地中一个成熟气田为例

在这项研究中，研究了早始新世（S1地层）的致密碳酸盐岩气藏，利用定量的地球物理方法，从巴基斯坦陆上中印度盆地的一个成熟气田中，对岩相估计和概率估计进行了储层物性预测。定量地震储层表征方法依靠基于井的岩相重新分类，带有偏移的振幅变化（AVO）属性分析和受定制的测井和地震数据（采集）条件约束的叠前同时反演属性。根据使用岩石物理特性对井数据的精确分析，估计了三种主要的岩石相（含烃的石灰岩，致密的石灰岩和页岩）。AVO属性（截距和坡度）可以方便地检查可能的AVO（I类），流体和岩性特征的振幅行为（反射系数）。使用基于井的岩相分类和反演地震属性估计可能的岩相（致密石灰岩和页岩）（贝叶斯框架中的叠前同步反演得到p阻抗和密度）。此外，岩石物性（粘土体积和孔隙度）是从概率神经网络方法获得的，该方法使用了测井曲线和叠前倒置属性（阻抗和密度），并限制了基于样本的地震属性（瞬时，窗口频率，滤波器，导数，积分和时间）。