Combining Nuclear Magnetic Resonance with Deep and Ultradeep Azimuthal Resistivity Images in Carbonate Reservoirs Links Reservoir Structure with Rock Type while Drilling

O. Ramadan, U. Idris, M. Van Steene, G. Santoso
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Abstract

Deep and ultradeep azimuthal resistivity images enable precise well placement inside the reservoir structure. However, they deliver limited information about the quality of the reservoir, especially in carbonates, where large pore-size variations are common. Combining the deep and ultra-deep resistivity images with logging-while-drilling (LWD) nuclear magnetic resonance (NMR) measurements enables linking reservoir structure with rock types while drilling for optimal well placement. The NMR data is used to generate four petrophysical rock types while drilling: RT-1 has good porosity and long T2 components, indicating large pores; RT-2 has good porosity but medium T2 components, indicating smaller pores; RT-3 has medium porosity and long T2 components; and RT-4 has medium or low porosity and medium or short T2 components, indicating the worst facies. The first step in identifying these rock types is running factor analysis on the NMR data. This data analysis method is used to reduce a large dataset to a smaller number of underlying components. Used with NMR data, the method typically produces 9 to 11 factors and their associated poro-fluid facies, which are further reduced to four to ease interpretation. The method was implemented in two wells. The first had a single lateral, which was geosteered using ultradeep azimuthal resistivity images and NMR. The borehole entered the reservoir from the bottom. The NMR indicated a large section of RT-4, so the well was steered to cross into the upper reservoir lobe in search of better rock type. The best rock type, RT-2, was discovered at 8 ft true vertical depth (TVD) below the top of the reservoir, and geosteering continued within that rock type. The second well was a trilateral, geosteered with deep azimuthal resistivity imaging and NMR measurements. The initial lateral penetrated the first reservoir layer, where the NMR indicated RT-3 rock type with high permeability. After about 500 ft of drilling, the target reservoir layer was identified below the wellbore, and the well was steered into it. The NMR initially indicated that the rock type was RT-2, but combining the reservoir structure from the deep azimuthal resistivity image inversion with NMR rock typing confirmed that the upper section of the second layer had the best rock type, namely RT-1. Based on this finding, the second and third laterals were placed in the upper part of the same reservoir layer, with an excellent net-to-gross ratio. Association of NMR rock typing and reservoir structure while drilling is a new methodology that combines the strengths of both techniques to optimize reservoir understanding and well placement.
碳酸盐岩储层核磁共振与深、超深方位电阻率成像相结合,将储层结构与岩石类型结合起来
深部和超深部方位电阻率成像可以在储层结构内部精确定位。然而,它们提供的有关储层质量的信息有限,特别是在碳酸盐岩中,在碳酸盐岩中,孔隙尺寸变化很大是很常见的。将深部和超深部电阻率图像与随钻测井(LWD)核磁共振(NMR)测量相结合,可以在钻井时将储层结构与岩石类型联系起来,从而实现最佳井位。利用核磁共振数据,钻探过程中生成了4种岩石物理类型:RT-1孔隙度好,T2组分长,孔隙较大;RT-2孔隙度好,T2组分中等,孔隙较小;RT-3孔隙率中等,T2组分较长;RT-4为中低孔隙度、中短T2组分,为最差相。识别这些岩石类型的第一步是对核磁共振数据进行因子分析。这种数据分析方法用于将大型数据集减少到较少数量的底层组件。与核磁共振数据一起使用时,该方法通常会产生9到11个因素及其相关的孔隙流体相,为了便于解释,这些因素进一步减少到4个。该方法在两口井中实施。第一个井只有一个侧向井,利用超深方位电阻率图像和核磁共振进行地质导向。钻孔从底部进入水库。核磁共振显示了RT-4的大剖面,因此,为了寻找更好的岩石类型,井被引导进入储层上瓣。最佳岩石类型RT-2在油藏顶部下方8英尺的真垂直深度(TVD)处被发现,地质导向在该岩石类型内继续进行。第二口井是三边井,采用深方位电阻率成像和核磁共振测量进行地质导向。初始侧向钻入第一储层,核磁共振显示为RT-3型高渗透率岩石。在大约500英尺的钻井后,目标储层在井眼下方被识别出来,并将井导向该储层。核磁共振初步显示岩石类型为RT-2型,但结合深部方位电阻率成像反演的储层结构与核磁共振岩石分型,确定第二层上部为最佳岩石类型RT-1型。基于这一发现,第二和第三条分支被放置在同一储层的上部,获得了良好的净总比。在钻井过程中结合核磁共振岩石类型和储层结构是一种新的方法,它结合了两种技术的优势,可以优化储层认识和井位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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