Hannah Wood, Andrew Barnett, Edward Follows, Priscila Ribeiro, Jaydip Guha, Josephine Wheeler, Runer Avila
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引用次数: 0
摘要
Barra Velha 地层的湖相碳酸盐岩是巴西桑托斯盆地丰富的碳氢化合物储层。在许多油田,这些碳酸盐岩由层状钙钛矿、球粒岩和灌木为主的岩层组成十米至米级的循环。然而,在局部地区,这些循环被十米级的重加工灌木粒岩/泥岩/角砾岩和原位灌木框架岩群所取代,沉积倾角较大(大于 30°)。后者可以有多种解释(如断层块高、碳酸盐丘),但综合地震、钻孔图像(BHI)测井和整个岩芯数据集,可以得出一个在湖泊环境中或湖泊边缘发育的碳酸盐丘升级模型。这项研究的岩心数据集显示了碳酸盐丘内独特的沉积结构。 从生产地质学的角度来看,以碳酸盐丘为主的岩层与以环状岩为主的岩层之间的关键区别在于它们的渗透结构。Cyclothem为主的层间显示出突出的横向连续性、十米尺度的垂直基质渗透率变化。而以丘陵为主的岩层缺乏细尺度的古水平分层,更多的是不规则的厘米级导管,垂直渗透率更高。只有将整个岩心样本与其他数据集整合起来,才能可靠地描述这种差异,并且这种差异会对扫描和生产性能产生重大的量化影响。
The Importance of Core for Carbonate Reservoir Evaluation: A Case Study from the Barra Velha Formation, Santos Basin, Brazil
The lacustrine carbonates of the Barra Velha Formation are a prolific hydrocarbon reservoir in the Santos Basin, Brazil. In many fields, they comprise decimetre- to metre-scale cycles composed of laminated calcimudstones, spherulite and shrub-dominated facies. However, locally these cycles are replaced by decametre packages of re-worked shrub grainstone/rudstone/breccia and
in-situ
shrub framestone with significant (>30°) depositional dips. The latter could be interpreted in several ways (e.g. fault block highs, carbonate mounds) but the integration of seismic, borehole image (BHI) log and whole core datasets converge on a model of aggrading carbonate mounds that developed in or marginal to a lake setting. The core datasets in this study demonstrate a distinctive depositional fabric within the carbonate mounds.
From a production geology standpoint, the crucial difference between mound-dominated and cyclothem-dominated successions is their permeability architecture. Cyclothem-dominated intervals show prominent and laterally continuous, decimetre-scale vertical matrix permeability variations. Mound-dominated intervals lack fine-scale palaeo-horizontal layering and exhibit a greater prevalence of irregular, cm-scale conduits and higher vertical permeability. This difference can only be reliably characterised via the integration of whole core samples with other datasets and has a significant quantified impact on sweep and production performance.