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Comparison of Data Sources to Constrain Fracture Intensity in Static Fracture Models

机译:数据源对静态断裂模型中裂缝强度的比较

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Quantitative representation of fracture intensity or its inverse, fracture spacing, in a static fracture model often comes from several data sources (core, borehole image logs, outcrop, and measures at the bit). These necessary constraints give inherently different quantitative assessments, due to differences in resolution and interpretative confidence. This is often imposed on an evaluation program by differing data types acquired during different stages of our exploration and development history of a reservoir due to changes in well path and borehole design. Our challenge is how to merge or meld differently acquired fracture quantifications to populate a quantitative static conceptual fracture model. This manuscript shows (1) the varying interpreted fracture intensity numbers derived from differing types of datasets, (2) the need for dataset calibration early in the development history of a fractured reservoir, and (3) the plan for laying out an appropriate data acquisition program to effectively utilize a multi-faceted fracture database in model construction and quantification from core, outcrop, and various borehole image logs. While there is great need to quantify fracture intensity to constrain of static fracture models for reservoir simulation and prediction, often we must have to merge disparate data types of inherently different resolution and error. This can be accomplished effectively if we gather appropriate calibration datasets. However, many of our calibration datasets fall short in doing the job in a quantitative sense. Proper calibration procedures are detailed.
机译:骨折强度的定量表示或其逆断裂间距的骨折模型常见于若干数据源(核心,钻孔图像日志,露头和测量)。由于解决方案和解释性信心的差异,这些必要的约束具有固有的不同定量评估。这通常是通过在我们的勘探和发展历史的不同阶段获得的不同数据类型来施加评估计划,由于井道和钻孔设计的变化,水库的发展历史。我们的挑战是如何合并或融合不同获得的骨折量化以填充定量静态概念性骨折模型。此手稿显示(1)从不同类型的数据集进行不同的解释性断裂强度编号,(2)在裂缝储存器的开发历史上提前需要进行数据集校准,以及(3)制定适当的数据采集的计划程序以有效地利用多面部骨折数据库,从核心,露头和各种钻孔图像原木中的模型构造和量化。虽然很有需要量化骨折强度来限制静态骨折模型进行储层模拟和预测,但我们必须必须合并隐生的数据类型固有的不同分辨率和错误。如果我们收集适当的校准数据集,这可以有效地完成。然而,我们的许多校准数据集在定量意义上进行工作缺乏。适当的校准程序是详细的。

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