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Multivariate Spatial Temporal Model of Gas Dynamic in Underground Gas Storage Based on Saturation Parameter from Well Logging Data

机译:基于饱和度参数的饱和度参数,基于饱和度参数的气体动力量的多变量空间时间模型

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The article considers the first successful example of the UGS operation process modeling based on a saturation parameter derived from well logging data in a multivariant space-time form.Well logging data is a valuable source of information on the actual gas distribution in the UGS reservoirs and on its dynamics during gas injection and production.In traditional hydrodynamic modeling,accounting for the existing dataset of a well logging control is problematic;traditional geometric interpolation algorithms either do not allow obtaining equally probable realizations of the modeled distribution,or require a priori setting of the trend model,which is normally impossible for solving this problem.Previously,no model could fully consider all available well logging control data.The initial modeling data is a sparse array of heterogeneous space-time structure with a priori unknown value distribution.The only currently known method that is able to obtain an equally probable set of realizations based on data with both arbitrary position and property distribution is the Amazonas method,which is based on a local prediction of properties based on robust statistics in a randomly located search window.Nowadays,the experience of the successful application of this method for multi-variant modeling of the reservoir property allocation has been accumulated.In this article,the possibility of its application to solve the issue of modeling the UGS gas dynamics was studied for the first time.The result of this research is a multivariant dynamic model of the UGS gas deposit,fully reproducing the entire array of well logging control data for several gas injection/production cycles.Although this type of model does not have the forecast power of a full-fledged simulation model,it helps solving problems going beyond the scope of the traditional hydrodynamic modeling.It can be used to identify areas of maximum uncertainty,which are prospective for setting up work on additional object exploration(including drilling new monitoring wells),to define discrepancies between the gas volumes carried as assets and the actual volumes of gas,to study gas dynamics and to perform multi-variant calculation of gas volumes in the reservoir in cases where,for one reason or another,it is impossible to build a traditional hydrodynamic model.
机译:该文章认为基于从多变量空间形式中的井记录数据导出的饱和度参数的第一成功示例.Well日志记录数据是关于UGS储存器中实际气体分布的有价值的信息来源在气体注入和生产过程中的动态。传统的流体动力学建模,井井测井控制的现有数据集的核算是有问题的;传统的几何插值算法不允许获得建模分布的同样可能的实现,或者需要先验趋势模型通常不可能解决这个问题。因此,没有模型可以充分考虑所有可用的井记录控制数据。初始建模数据是具有先验未知值分布的异构时空结构的稀疏阵列。目前已知的方法能够获得同样可能的一组识别Ba SED ON与任意位置和属性分布的数据是Amazonas方法,它基于基于在随机定位的搜索窗口中基于鲁棒统计的局部属性预测.NowAdays,该方法对多变体方法的成功应用程序的经验储存物业分配的建模已被累积。本文首次研究了解决UGS气体动力学的建模问题的可能性。本研究的结果是UGS气体的多变量动态模型存款,完全再现整个井测井控制数据阵列进行多个气体注入/生产周期。虽然这种类型的模型没有完整的模拟模型的预测力量,但它有助于解决传统范围的问题超出了传统的范围流体动力学建模。它可用于识别最大不确定性的领域,这是在额外的对象探索上建立工作的前瞻性(包括钻井新的监控井),以定义作为资产和实际气体的气体卷之间的差异,以研究气体动力学,并在一个原因或另一个,建立传统的流体动力学模型是不可能的。

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