Estimation of reservoir properties using pre-stack seismic inversion and neural network in mature oil field, Upper Assam basin, India

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Applied Geophysics Pub Date : 2024-09-20 DOI:10.1016/j.jappgeo.2024.105523
Pawan Kumar Singh , Uma Shankar
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Abstract

The mature oil fields require comprehensive characterization for enhanced hydrocarbon production, and subsequently demands estimation of reservoir properties. The key properties viz. volume of clay, effective-porosity, hydrocarbon-saturation has been evaluated for an aging Oligocene reservoir of Upper Assam basin, located in northeastern India from seismic and well log data. Elastic properties (acoustic and shear impedance) and density are derived from pre-stack inversion of 3D seismic data. These elastic properties are analyzed for their sensitivity for discrimination of lithology and fluid-content, and many derived attributes are computed from elastic properties. These attributes are assessed for their predictability to predict the target reservoir properties using multi-attribute analysis. For each of the target property neural network is trained with the most predictable attributes, and multi-dimensional, non-linear neural network models are created using multilayered feed forward neural network (MLFN), followed by Probabilistic neural network (PNN). The specific neural network models for each target property are employed for quantitative estimate of volume of clay, effective-porosity, hydrocarbon-saturation in inter-well regions. The estimated properties leverage the identification of untapped oil reserves and provide promising opportunity for enhanced production through drilling of infill wells.
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利用叠前地震反演和神经网络估算印度上阿萨姆盆地成熟油田的储层性质
成熟油田需要全面的特征描述以提高碳氢化合物的产量,因此需要对储层属性进行估算。根据地震和测井数据,对位于印度东北部的上阿萨姆盆地一个老化的渐新世储层的关键属性,即粘土体积、有效孔隙度、碳氢化合物饱和度进行了评估。弹性属性(声阻抗和剪切阻抗)和密度来自三维地震数据的叠前反演。分析了这些弹性属性对区分岩性和流体含量的敏感性,并根据弹性属性计算了许多衍生属性。利用多属性分析评估这些属性的可预测性,以预测目标储层属性。针对每种目标属性,使用最可预测的属性训练神经网络,并使用多层前馈神经网络(MLFN)和概率神经网络(PNN)创建多维非线性神经网络模型。针对每种目标属性的特定神经网络模型被用于定量估算井间区域的粘土体积、有效孔隙度和碳氢化合物饱和度。估算出的属性有助于确定未开发的石油储量,并为通过钻探填充井提高产量提供了大好机会。
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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