Sweet Spot Identification Study in Unconventional Gas Play in UAE

This paper builds upon the methodology for identifying natural fractures in carbonate reservoirs utilizing the mega onshore seismic data based on the borehole imager and advanced mud logging data combined with the seismic inversion results. The approach is applied to new areas of Diyab unconventional gas play that feature post-stack seismic inversion data, including porosity and acoustic impedance volumes, alongside newly drilled wells with long-term well tests. The objective is to evaluate the robustness, adaptability, and predictive power of the methodology across varying geological conditions and reservoir properties to be used as a sweet spot identification technique for the development drilling. The workflow combines seismic inversion attributes with structural interpretation and borehole image log calibration to delineate fracture-prone zones. The methodology was adapted to incorporate changes in seismic response across the field and adjusted using data from long well tests and new image logs. Key inversion parameters—such as low impedance and relatively high porosity anomalies—were cross-analyzed with different structural attributes. Hard data calibration based on advanced mud logging was applied to refine the fracture detection algorithm. A comparative analysis between the original calibration area and new zones helped to assess transferability of key fracture indicators and define limits of extrapolation. The extended application of the methodology to newly drilled areas demonstrated a high degree of consistency between predicted fracture zones, high porosity anomalies and dynamic well test results. In several cases, wells that intersected zones flagged as fractured by the seismic model exhibited enhanced productivity, stable well head pressure and relatively low decline rate. Notably, areas with anomalously low impedance proved to be reliable indicators of sweet spots, particularly when supported by fracture anomalies linked to the connected fracture networks. Refining the thresholds for the fracture attribute extraction improved the predictive accuracy of the model under different reservoir conditions. The integration of long-duration well test data played a crucial role in validating the spatial continuity and hydraulic connectivity of the interpreted fracture zones. The findings highlight the potential for seismic inversion data to enhance fracture prediction in unconventional carbonate fields and support more targeted infill drilling and reservoir management strategies. This work demonstrates the practical scalability of a previously published fracture identification workflow by extending it to new areas of a producing carbonate reservoir. By integrating seismic inversion products with dynamic test data and borehole image interpretation, the study provides a validated, transferable framework for field-wide fracture characterization. The refined methodology contributes to better decision-making in reservoir development, especially during development drilling of the unconventional assets where new wells and updated seismic attributes offer opportunities for model calibration and optimization. The work aligns with ADIPEC's focus on maximizing recovery through innovation, data integration, and applied geoscience in complex subsurface environments.