Abstract In low-resistivity reservoirs characterized by thinly laminated sand-shale sequences, a substantial portion of hydrocarbon reserves remain under explored due to the challenges in accurately identifying these formations. These reservoirs exhibit electric anisotropy, often causing conventional logging tools to misinterpret hydrocarbon-bearing sands as water-bearing zones. Advanced multi-component/triaxial induction logging technology offers a solution by providing detailed resistivity measurements in multiple directions, enabling better resolution of these complex reservoirs through measurement of directional resistivity patterns, formation dip, and azimuth. This study integrates multi-component induction measurements including inverted vertical (Rv) and horizontal (Rh) resistivities, dips, and quality indicators (QIs) with complementary sensor data such as compensated array resistivities, high-resolution resistivity images, and formation tester fluid sampling to characterize reservoir properties. Multi-component induction measurements corrected for borehole and shoulder-bed effects via radial and vertical 1D inversion algorithms respectively, enhanced the accuracy of formation parameter estimation. In addition to petrophysical interpretation, Dips derived from multi-component induction measurements offer valuable insight into sedimentary and structural orientations. The newly developed Quality Indicators (QIs) synthesize all available data to robustly evaluate the reliability of these dips, enhancing structural interpretation. Corrected multi-component induction measurements correlate strongly with triple-combo logs and image logs, validating the identification of anisotropic zones by analyzing the separation between Rv and Rh. Resistivity anisotropy is prominent in argillaceous intervals with significant Rv-Rh separation and diminishes as shale volume decreases. Sand-shale intercalations in arenaceous intervals also exhibit increased anisotropy, reflected in elevated Rv/Rh ratios, whereas clean, homogenous formations show minimal anisotropy. Beyond lithology, fluid saturation influences resistivity anisotropy. Water-saturated laminated sand zones display low anisotropy, while gas- or oil-saturated zones exhibit greater Rv-Rh separation. This fluid-dependent anisotropy supports the use of multi-component induction measurements to estimate hydrocarbon saturation more accurately, especially where conventional logs underestimate pays due to limited vertical resolution. Applying the Thomas-Stieber model allows decomposition of laminated intervals into volumetric contributions of clean sand and laminated-dispersed shale. Incorporating laminated shale volume with Rv and Rh in Tensor Resistivity model yields a more precise estimate of sand resistivity (Rsand), which is typically masked in laminated sequences. Dip and azimuth derived from multi-component induction measurements align well with manual picks and reveal structural complexities even in challenging geological settings, underscoring the value of dips derived from multi-component induction measurements when borehole images are unavailable. This integrated approach significantly improves reservoir characterization by resolving anisotropic zones and providing insights into subsurface structures, thereby enhancing formation evaluation accuracy. Overall, the study demonstrates that resistivity anisotropy derived from multi-component induction measurements provides valuable insights into lithological and sedimentary structures, as well as fluid distribution within the formation. This improved understanding is especially valuable in laminated and anisotropic reservoirs. The ability of multi-component induction measurements to resolve both horizontal and vertical resistivities also enables enhanced saturation computation, leading to more accurate hydrocarbon quantification in formations. This advancement is critical not only for maturing basins where new discoveries are increasingly challenging and economic exploitation of subtle reservoirs is essential but also valuable in frontier and unexplored settings where structural and sedimentological insights are limited by data availability.
Ngui et al. (Mon,) studied this question.
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