Tapping into low resistivity (LR) pay zones in brown fields requires less capital expenditure (CAPEX) than developing new fields or embarking on enhaced oil recovery (EOR). Therefore, the economic significance of commonly overlooked LR thin-bed reservoirs drew significant attention from asset managers, particularly in the current rapidly declining economics of the hydrocarbon (HC) business of today. LR reservoirs however, are difficult to identify. Therefore, they can easily be bypassed unless a comprehensive identification program is employed. Identification, evaluation and quantification of LR reservoirs require integration of different workflows that address different aspects of geophysical, geological, petrophysical and reservoir engineering challenges. In this study, a workflow and case study for selection of key wells with the highest potential of by-passed LR reservoirs and integration of computational workflows, with appropriate algorithms, are demonstrated for the monetisation of LR pay zones.

We are proposing a systematic approach to identify LR-prone areas of a given basin or field prior to screening petrophysical data for verification and evaluation. The approach first identifies areas where depositional processes are conducive for developing LR reservoirs. For example, areas associated with channels that may have well-developed crevasse splays. These potential LR areas are then selected for more detailed petrophysical investigation to confirm the potential presence of LR pay. If available, bypassed but potentially productive zones can be verified with advanced logging data using integrated algorithms. The elimination of unnecessary testing in parts of a basin, or field, having low potential for LR pay zones, yields the sought-after gains in economics and augments the concept of a "financially less demanding" project.

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