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The inclusion of dynamic constraints in stochastic conditional simulation.

机译:随机条件仿真中包含动态约束。

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摘要

Throughout the life of a petroleum reservoir, information about the reservoir--typically seismic, core and log data, rates and pressures--is collected. These data represent perspectives of the reservoir at widely-differing scales. The challenge therefore, is to integrate these different scales of data into a reservoir description process while honoring the scale level they represent. In addition, algorithmic efficiency may prevent the consideration of certain types of data, for example 'dynamic' data, such as rate and pressure data. These represent valuable information because they represent a heterogeneity scale which is closer to simulation gridblock scales than the typical well core/log data. So far, dynamic data have been used on a limited basis because it then becomes necessary to use flow simulation, making the algorithm inefficient.;An efficient algorithm for using stochastic conditional simulation in which we directly incorporate dynamic information--namely, rate and pressure information is presented. Modeling the spatial description on a fine scale and the flow on an upscaled grid reduces the flow simulation execution time and allows for a faster conditional simulation algorithm. Upscaling approaches are described which result in flow performance matching between the fine or 'true' scale and the upscaled grid. The simulated annealing method is used with a 2-part objective function consisting of a variogram constraint and a flow simulation constraint with each part being appropriately weighted.;Improvements in the reservoir description when the dynamic constraint is included are demonstrated. Also the upscaling techniques used are shown to be effective in matching the flow performance of the reservoir grid between scales. The procedure simultaneously generates fine scale description which can be used for future evaluation, and a coarse scale description which honors the production data. Using this procedure, up to a 10,000-gridblock description has been simulated.
机译:在石油储层的整个生命周期中,都会收集有关储层的信息(通常是地震,岩心和测井数据,速率和压力)。这些数据代表了储层在不同尺度下的观点。因此,挑战在于将这些不同比例的数据整合到储层描述过程中,同时要尊重它们所代表的比例级别。此外,算法效率可能会阻止考虑某些类型的数据,例如“动态”数据,例如速率和压力数据。这些代表了有价值的信息,因为它们代表的异质性尺度比典型的井芯/测井数据更接近于模拟网格块尺度。到目前为止,由于在有限的基础上使用了动态数据,因为随后有必要使用流量模拟,从而使该算法效率低下。;使用随机条件模拟的一种有效算法,其中我们直接合并了动态信息,即速率和压力提供信息。对空间描述进行精细建模,并在高比例网格上对流进行建模,可以减少流模拟的执行时间,并允许更快的条件模拟算法。描述了按比例放大的方法,这些方法导致精细或“真实”比例与按比例放大的网格之间的流动性能匹配。采用模拟退火方法,将目标函数分为两部分:目标函数包括方差图约束和流量模拟约束,并分别对各个部分进行适当加权。展示了在包含动态约束的情况下对储层描述的改进。而且,所显示的放大技术也显示出在比例尺之间匹​​配储层网格的流动性能方面有效。该过程同时生成可用于将来评估的细粒度描述,以及生成尊重生产数据的粗粒度描述。使用此过程,已模拟了多达10,000个网格块的描述。

著录项

  • 作者

    Gajraj, Allyson.;

  • 作者单位

    The University of Tulsa.;

  • 授予单位 The University of Tulsa.;
  • 学科 Engineering Petroleum.
  • 学位 Ph.D.
  • 年度 1996
  • 页码 233 p.
  • 总页数 233
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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