New Approach for Stress-Dependent Permeability and Porosity Response in the Bakken Formation

Abstract

Abstract During the reservoir depletion and injection operations, the net effective stress is disrupted due to pore pressure changes. As a result, the reservoir properties, mainly porosity and permeability, are influenced by the change in the stress behavior in the reservoir rock. Understanding the porosity and permeability stress-dependent alteration is crucial since it directly impacts the reservoir storage capacity and the production/injection capabilities. Conventionally, lab experiments are conducted to understand the stress dependency of porosity and permeability magnitudes. Two methods are usually used: the unsteady-state method (Core Measurement System, CMS-300) and the steady-state method (Core Measurement System, CPMS). The challenges with these experiments reside in the fact that they are expensive and time-consuming and may cause the destruction of the core samples due to the applied stresses. This study aims to investigate the effect of stress variations on porosity and permeability changes. These properties were measured on a total of 2150 core data from the three members of the unconventional Bakken formation (upper, middle, and lower), applying 35 different Net Confining Stress (NCS) values, ranging from 400psi to 5800psi. A correlation was formulated between permeability and the NCS to illustrate the stress dependency relationships. The Grey Wolf Optimization algorithm (GWO) was used to tune the correlation for the Bakken formation. Machine Learning methods were also applied for the porosity and permeability stress dependency response prediction, which are as follows: Linear Regression (LR), Random Forest Regression (RF), XGBoost Regression (XGB), and Artificial Neural Network (ANN). The results demonstrate that the porosity and the permeability decrease with the increase of the NCS and vice versa. The permeability is highly sensitive to the NCS changes compared to the porosity. The developed correlations showed a good fit with the data extracted from the laboratory experiments of the pilot well. For the data-driven models, the coefficient of correlation R2-Score ranged from 91% to 93%. These models can be used to constrain the modeling work and reduce the uncertainties by introducing the effect of the net effective stress changes during reservoir depletion/injection on petrophysical properties.