Prediction of Two-Phase Flow Relative Permeability in Discrete Fractures

Considering carbonate oil reservoirs, a rock fracture is a planar-shaped void filled with oil, water, gas and/or rock fines. These fractures vary in scale forming connected and complex networks of fractures. They have an effect on deliverability of fluids depending on their geometrical complexity, extent, matrix-fracture interaction, wettability, and orientation. In fractured reservoir rocks, relative to the rock matrix, fractures form highly permeable flow pathways that dominate fluid flow and transport in the reservoir which might have favorable or non-favorable effects on hydrocarbon production. It is crucial to characterize the fluid flow in the fracture networks to examine the root-cause relationships, the impact on hydrocarbon recovery and quantify the efficiency of enhanced recovery mechanisms. This work describes the development of a machine learning model for history matching and predicting two-phase relative permeability. Capitalizing on the main principles of the 4th Industrial Revolution (IR 4.0), the development of this model was achieved by training machine learning (ML) algorithms and using advanced predictive data analytics on data collected from lab experiments as input. The model derived from the analysis describes two-phase flow of oil and water in a single discretized fracture taking into account fracture aperture, wall roughness, orientation and, flow rates and direction. It also accommodates fluids and fracture characteristics to match laboratory SCAL experimental of co-current oil and water flow in a mixed-wettability single fracture modeled as narrow gap in a Hele-Shaw cell. The experimental data exhibit variations in shape and end-points that mainly reflect the effects of fracture aperture, roughness, inclination, and hysteresis effects. This in turn demonstrate the effects of phase interference, saturation changes, and major forces acting on two-phase flow in fractures like capillary and viscous forces. The empirical relationship showed an acceptable match to the experimentally derived relative permeability in most of the cases as well as good predictive capabilities against the blind tests on other sets of experimental data and numerical simulation models. Having both fracture relative permeability data (describing the fluids flow) and detailed fracture characterization improves our understanding of the reservoir dynamics and fractured network impact on hydrocarbon recovery.