Balancing Early Recovery and Long-Term Value in Stress-Sensitive Reservoirs

Abstract

This study investigates how geomechanics influences long-term productivity in an ultra-tight black-oil reservoir. Matrix rock properties are generated using a Bayesian framework in which porosity is sampled from a prescribed prior distribution, and permeability is sampled from a conditional distribution based on the Kozeny-Carman correlation. Porosity and permeability in the stimulated reservoir volume (SRV), and conductivity in the hydraulic fracture, are modeled using uniform probability distributions. Geomechanical effects are captured through an exponential pressure-dependent function that retains a non-zero lower permeability bound. This correlation is controlled by two parameters, each governed by a probability distribution. A Monte Carlo simulation approach was implemented to evaluate the impact of geomechanics and pressure-drawdown strategies on well productivity in the presence of uncertainty. Simulation results indicate that geomechanics initially reduce oil, gas, and water production relative to the geomechanics-free baseline model. During early-to-intermediate production, oil and water production recover and slightly exceed baseline predictions by 3.65–8.28% and 7.51–8.00%, respectively, whereas gas production remains 3.4–3.8% below the baseline prediction. These results suggest that stress-sensitive permeability may delay gas liberation and production by sustaining higher reservoir pressure for a longer period.