核磁共振因子分析、多功能随钻测井和T2建模相结合,提高了复杂碳酸盐岩储层流体识别能力

D. Permanasari, Z. Ernando, Taufik Nordin, Azlan Shah B Johari, Fierzan Muhammad
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引用次数: 0

摘要

碳酸盐环境本质上是复杂的,由于孔隙的非均质性,基于其岩石物理性质的表征一直具有挑战性。在本文中,我们介绍了应用于随钻核磁共振(NMR)数据的因子分析、来自多功能随钻测井(LWD)工具的全套数据以及NMR T2横向弛豫时间建模的集成,以提高复杂碳酸盐岩储层的流体类型解释。解释结果对于大角度开发井的射孔和完井决策至关重要。本案例研究的碳酸盐岩储层位于东爪哇盆地的Kujung组。库井ⅰ为块状碳酸盐岩储层,次生孔隙丰富,库井ⅱ和库井ⅲ为薄层碳酸盐岩储层与页岩层互层。由于储层中存在多种流体、低矿化度以及孔隙非均质性和成岩作用的影响,Kujung II和III层的流体类型识别存在很大的不确定性。由于井眼角度大,LWD工具传输成为数据采集的主要方法。随钻核磁共振和多功能随钻测井工具在同一个钻井底部钻具组合(BHA)上下入,以提供完整的地层评价和流体识别。利用核磁共振因子分析技术将T2的分布分解为其孔隙流体成分。进行了彻底的T2峰建模,以解释因子分析结果中的流体特征。通过对井眼图像、井径仪、三重组合、密度磁共振气体校正孔隙度(DMRP)以及延时数据进行评估,以确定次生孔隙度的存在,并缩小T2流体特征解释的范围。每一种孔隙流体特征都在Kujung 1地层中进行了识别和验证,其中包括已探明的天然气和厚水层。然后将这些特征作为参考来解释Kujung II和III地层的流体类型。在400 ms至1 s的较短T2范围内,通过一个低振幅峰来识别气体。油基或合成油基泥浆(SOBM)滤液在较长的T2范围内(>1.5 s)表现为一个高振幅峰值。水特征在很大程度上取决于下伏孔隙大小。孔隙尺寸越大,T2值越长,与油气处于相同的T2范围。因此,将核磁共振孔隙流体特征解释与其他随钻测井数据结合起来,以限制流体类型的可能性,这一点非常重要。该综合方法成功地改善了Kujung II和III薄碳酸盐岩储层目标的流体类型解释,并得到了同一口井的实际生产结果的证实。该案例研究展示了随钻核磁共振与其他随钻数据的出色整合,以减少复杂碳酸盐岩储层中流体类型的不确定性,这些不确定性是常规解释方法无法解决的。在此基础上,类似的综合核磁共振因子分析方法可以应用于同一油田的未来开发井。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of NMR Factor Analysis, Multifunction LWD Measurements, and T2 Modeling Improve Fluid Identification in Complex Carbonate Reservoirs
Carbonate environments are complex by nature and the characterization, based on their petrophysical properties, has always been challenging due to the pore heterogeneity. In this paper, we present the integration of factor analysis applied to while-drilling Nuclear Magnetic Resonance (NMR) data, full-suite data from a multifunction logging-while-drilling (LWD) tool, and modeling of the NMR T2 transverse relaxation time to improve the fluid typing interpretation in complex carbonate reservoirs. The interpretation results are essential for perforation and completion decisions in a high-angle development well. The carbonate reservoirs in this case study are within the Kujung formation in the East Java Basin. Kujung I is a massive carbonate reservoir with abundant secondary porosity, while Kujung II and III consist of interbedded thin carbonate reservoirs and shale layers. High uncertainty in identifying the fluid type existed in the Kujung II and III formations due to the presence of multiple fluids in the reservoir, the effect of low water salinity, as well as pore heterogeneity and diagenesis. Due to the high-angle well profile, LWD tool conveyance became the primary method for data acquisition. NMR while drilling and multifunction LWD tools were run on the same drilling bottomhole assembly (BHA) to provide complete formation evaluation and fluid identification. The NMR factor analysis technique was used to decompose the T2 distribution into its porofluid constituents. Thorough T2 peaks modeling was performed to interpret the fluid signatures from the factor analysis results. Borehole images, caliper, triple-combo, density-magnetic resonance gas corrected porosity (DMRP), as well as time-lapse data were evaluated to identify the presence of secondary porosity and narrow down the T2 fluid signatures interpretation. Each of the porofluid signatures were identified and validated in the Kujung I formation with its proven gas and thick water zone. These signatures were then used as references to interpret the fluid types in the Kujung II and III formations. Gas was identified by a low-amplitude peak in the shorter T2 range between 400 ms to 1 s. Oil or synthetic oil-based mud (SOBM) filtrate was indicated by a high-amplitude peak in the longer T2 range (>1.5 s). The water signatures are very much dependent on the underlying pore sizes. Larger pore sizes will generate longer T2 values, which could fall into the same T2 range as hydrocarbon. For that reason, it is important to combine the NMR porofluid signatures interpretation with other LWD data to restrict the fluid type possibilities. This integrated methodology has successfully improved the fluid type interpretation in the Kujung II and III thin carbonate reservoir targets and was confirmed by the actual production results from the same well. This case study presents excellent integration of LWD NMR with other LWD data to reduce fluid type uncertainties in complex carbonate reservoirs, which were unresolved by conventional interpretation methods. Based on this success, a similar integrated NMR factor analysis method can be applied to future development wells in the same field.
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