Application of improved data-driven diagnostics workflow based on coupled hydro-mechanical model for stimulation candidate selection in a complex carbonate reservoir

Abstract The focus of the present paper is on utilizing a comprehensive diagnostics workflow that combines coupled hydro-mechanical modeling with production data-driven for optimization stimulation candidate selection process. Reservoir fluids and production-induced depletion affect reservoir mechanical environment due to reduced pore pressure increased effective stress consequent porosity permeability reduction, depending rock sensitivity changes. formation damage index (CFDI) implemented in traditional capture effect changes near-wellbore over time. top potential candidates are recognized based heterogeneity index, static CFDI parameter. dynamic parameter through estimating time-dependent induced by state fluid pressure. Probabilistic type curves decline curve analysis wells versus unit also applied complement enhancement selection.