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

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

Automated summary

This paper focuses on using a comprehensive diagnostics workflow that combines coupled hydro-mechanical modeling with production data-driven diagnostics to optimize the stimulation candidate selection process. The coupled formation damage index (CFDI) is used to capture the effect of production-induced stress changes on near-wellbore permeability over time, helping identify the top stimulation candidates. Probabilistic type curves and decline curve analysis of candidate wells versus reservoir unit are also utilized to complement stimulation and production enhancement candidate selection.