Early identification of low resistivity pay (LRP) reservoir is vital in assessing its prospect and capability. Productive reservoirs may exhibit low resistivity and consequently, their potential is simply overlooked. Remapping these intervals can have significant production and reserve implications. Traditionally, resistivity logs are used to identify pay intervals due to the resistivity contrast between oil and formation water. However, when pay intervals exhibit low resistivity, such logs return low confidence in defining hydrocarbon potential.
Due to the complexity of low resistivity pay (LRP), its cause and proper mitigation should be determined prior to applying a solution. Researchers have identified several reasons responsible for this occurrence; among which are the presence of heterogeneous pore structures specifically micro-porosity, fractures, paramagnetic minerals, and deep conductive mud invasion.
Almost all preceding publications assume a technique will work but not the other. However, this is the first time, to our knowledge; an integrated approach is used to develop LRP assessment workflow. We have integrated the information coming from geology (e.g., thin-section, XRD), formation pressure and well tests, NMR, MICP, and dean stark data. The integration successfully identified and remapped the carbonate low resistivity reservoir. This model was validated in an appraisal well on Abu Dhabi mainland, for that an extended data was acquired.
Thereafter, the integrated LRP model was compared with the computed water saturation from conventional resistivity tools. The validation was successful in terms of confirming the prognosis. Interpreting the results from the multidisciplinary integrated model confirms a deeper Free Water Level (FWL), hence oil pool extension. Further analysis showed that the causes of LRP in this considered formation was limited to presence of micro-porosity and high saline mud invasion.