Tight naturally fractured carbonate reservoirs often show steep rate and pressure declines leading to reduction in apparent well productivity. These declines are influenced by several complex phenomena. Some of these are geometry and density of natural fractures (NFs), matrix properties, and damage that occurs during the drilling and completion operations. An additional factor which is sometimes overlooked is the change in NF properties (such as decrease in NF permeability) with time that occurs because of production drawdown. Modeling this phenomenon forms the focus of this paper.

Injection or production of fluid in naturally fractured reservoirs (NFRs) typically causes slippage, opening, and fracture compliance effects that depend on elevation or decrease of fluid pressure in the rock. In this work, we solve equations of fluid transport in a rock with a high density of NFs using constitutive equations for hydro-mechanical fracture response to pressure changes and chemical reactions with the injected acids. Followed by the acid treatment and shut-in periods, we model the production phase. For the production modeling, we specified a flowing bottomhole pressure (FBHP) and studied the production rate decline as a function of time.

The production decline is a result of NF permeability decrease, which is due to the increase of effective stresses applied to NFs. We show sensitivity of these production signatures to spatial density of NFs and viscosity of treatment fluids. We also demonstrated changes of anisotropic formation permeability tensors both after the shut-in and during production for two types of stimulation: mechanical and chemical formation damage.

Common reservoir engineering workflows sometimes ignore well-known time-dependent rock mechanics behavior of NFs. The work presented in this paper will help quantify NF conductivity loss with producing time which is an important parameter in numerical reservoir simulations. Decline rates in tight NFRs are steep and impact EURs and project economics. Therefore, understanding the mechanisms that influence these declines through fit-for-purpose modeling and simulation is a crucial step for optimizing their development.

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