首页> 外文会议>Proceedings of The 38th IPA convention and exhibition-Strengthening Partnership to Enhance Indonesia’s Energy Resilience and Global Competitiveness >BASIN-SCALE ROCK PHYSICS STUDY AND STOCHASTIC AVO MODELLING FOR THE DEEP WATER KUTEI BASIN (EAST KALIMANTAN – INDONESIA)
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BASIN-SCALE ROCK PHYSICS STUDY AND STOCHASTIC AVO MODELLING FOR THE DEEP WATER KUTEI BASIN (EAST KALIMANTAN – INDONESIA)

机译:深水库蒂盆地(东加里曼丹-印度尼西亚)的盆地尺度岩石物理研究和随机AVO模拟

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A rock physics study conducted on twenty two (22)rnwells from the deep water Kutei Basin (EastrnKalimantan – Indonesia) was carried out to analysernthe elastic properties on a basin-scale level andrngenerate stochastic AVO modelling for fluid andrnlithology prediction. The creation of an AVOrnreference model based on measured data is deemedrnof great importance for prospect generation deriskingrnof the field. The methodology consisted inrnthe execution of two phases. The first phaserninvolved the analysis and quality control of thernwell-log elastic properties distribution, thernidentification of six litho-fluid classes (normalrnpressure shales, brine sands, oil sands, gas sands,rnoverpressured shales and vulcanites) and fluidrnreplacement modelling to create different fluidrnscenarios for the sands. Then, depth trend analysisrnwas carried out well by well for each elasticrnproperty: density, P-wave velocity and S-wavernvelocity with variability and correlation analysisrnperformed. The second phase relied on the use ofrnMonte Carlo simulation to generate a large numberrnof elastic properties realisations for each litho-fluidrnclass and then calculate AVO intercept and gradientrnto be used for analysis. The study highlighted thatrnthe key factor driving the elastic behaviour is thernburial depth. Modelling the elastic behaviour ofrn“anomalous” lithologies (vulcanites andrnoverpressured shales), which are also present in thernbasin, allowed an understanding of their theoreticalrnimpact on the capability to discriminaternhydrocarbon bearing sands.
机译:在来自深水库提盆地(东加里曼丹–印度尼西亚)的二十二(22)口井中进行了岩石物理研究,以分析盆地规模水平的弹性,并生成了随机AVO模型,用于流体和岩性的预测。基于测量数据的AVOrnreference模型的创建被认为对于该领域的潜在客户产生非常重要。该方法包括两个阶段的执行。第一阶段涉及对测井弹性特性分布的分析和质量控制,对六种岩性流体类别(常压页岩,盐水砂,油砂,气砂,超压页岩和火山岩)的识别以及流体置换模型,以创建不同的流体情景。金沙。然后,针对每个弹性特性,分别对密度,纵波速度和横波速度进行了深度趋势分析,并进行了变异性和相关性分析。第二阶段依赖于使用Monte Carlo模拟来为每个岩石流体类生成大量的rnof弹性特性实现,然后计算AVO截距和梯度以用于分析。研究强调,影响弹性行为的关键因素是埋葬深度。对“异常”岩性(火山岩和超压页岩)(也存在于盆地中)的弹性行为进行建模,可以了解它们对区分含烃砂岩能力的理论影响。

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