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A Reservoir Conductivity Evaluation Using Percolation Theory

机译:基于渗流理论的储层电导率评价

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Oil reservoirs are very complex with geological heterogeneities that appear on all scales. Proper modeling of the spatial distribution of these heterogeneities is crucial, affecting all aspects of flow and, consequently, the reservoir performance. Reservoir connectivity and conductivity evaluation is of great importance for decision-making on various possible development scenarios including infill drilling projects. This can be addressed by using the percolation theory approach. This statistical approach considers a hypothesis that the reservoir can be split into either permeable (good sands) or impermeable flow units (poor sands) and assumes that the continuity of permeability contrasts controls the flow. The approach uses an object-based technique to model the spatial distribution of both isotropic and anisotropic sand bodies in two dimensions. Recently the application of the percolation approach in evaluating reservoir connectivity for both conventional and fracture reservoirs has been reported. This article concentrates on analyzing the conductivity behavior of petroleum reservoirs. In particular, the percolation approach is used to develop the universal curves of the average reservoir permeability and its associated uncertainties. To validate the approach, we used the Burgan reservoir dataset of Norouz offshore oilf ield in the south of Iran. Core and palynological data analysis indicated that the Burgan consists of a series of incision-fill sequences occurring in an estuarine/coastal plain/deltaic environment. Consequently, the Burgan consists of a thick stack of excellent quality sands incising into each other with few remaining shalier sediments locally separating these sequences. We have shown that the effective permeability, as a measure of reservoir conductivity, from the percolation approach gives reliable prediction once comparedwith exact numerically obtained results from the modeling of real field data. Moreover, the first approach as obtained from algebraic manipulation is very fast whereas the latter is so costly and time consuming.
机译:储油层非常复杂,具有各种规模的地质异质性。对这些非均质性的空间分布进行正确建模至关重要,这会影响流量的各个方面,进而影响储层的性能。储层连通性和电导率评估对于各种可能的开发方案(包括填充钻探项目)的决策至关重要。这可以通过使用渗流理论方法来解决。这种统计方法考虑了一个假设,即储层可分为渗透性(优质砂)或不可渗透性流动单元(劣质砂),并假设渗透性对比的连续性控制着流量。该方法使用基于对象的技术来模拟二维各向同性和各向异性砂体的空间分布。最近,已经报道了渗流方法在评估常规和裂缝性储层的储层连通性中的应用。本文着重分析石油储层的电导率行为。特别是,使用渗流方法来开发平均油藏渗透率及其相关不确定性的通用曲线。为了验证该方法,我们使用了伊朗南部Norouz近海油田的Burgan油藏数据集。核心和孢粉学数据分析表明,伯根河口由一系列在河口/沿海平原/三角洲环境中发生的切缝填充序列组成。因此,伯根大地由厚厚的优质沙子叠层组成,它们相互切开,几乎没有残留的较滑落的沉积物将这些层序局部分开。我们已经表明,渗透率作为渗流方法的一种有效的渗透率,与真实油田数据建模得到的精确数值结果相比,可以提供可靠的预测。而且,从代数运算获得的第一种方法非常快,而后者则非常昂贵且耗时。

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