Integrated Static–Dynamic Analysis for Sweet Spot Identification and Reserves Prediction in Low-Permeability Reservoirs Using Production Data and Geospatial Attributes

Abstract

The optimization of infill well placement and the reliable prediction of Estimated Ultimate Recovery (EUR) continue to represent critical challenges in low-permeability reservoir. Conventional permeability evaluation using Pressure Build-Up tests is often impractical due to prolonged shut-in requirements and operational constraints. Although geospatial tools such as Antelope Map effectively identify fracable zones associated with high initial production rates, sustained long-term recovery is not necessarily guaranteed. This study proposes an integrated static–dynamic framework that combines geospatial attributes with production-based analysis to improve reservoirs characterization and well placement decisions. In-situ permeability and flow capacity, expressed as , are extracted directly from routine production data of 14 hydraulically fractured wells using the Inverted Decline Curve method, thereby bypassing the limitations of pressure transient analysis. The Stretched Exponential Production Decline model is subsequently applied to generate bounded and realistic EUR predictions. Pearson correlation analysis shows a weak relationship between permeability derived from static logs and EUR. In contrast, the production-derived  the parameter shows the strongest positive correlation, reflecting effective flow capacity and the influence of matrix heterogeneities such as trace fossils. By comparing the  distribution with the static Antelope Map, this dual-criteria approach helps identify sweet spots that support both favourable fracability and more sustained fluid delivery. In general, this data-driven workflow provides a practical alternative framework to reduce geological uncertainty and optimize well placement in low-quality reservoirs.