combining voronoi triangulation discrete fracture network (dfn) models with fractal dimension analysis of complex sequences for predicting porosity and permeability

Optimizing reservoir performance in fractured reservoirs relies heavily on understanding and harnessing fracture connectivity at the reservoir scale. voroni triangulation discrete fracture network (dfn) models offer a unique depiction of fractures and their connectivity compared to other methods. petrophysical property modeling involves various algorithms, with dfn emerging as a novel mathematical approach. this study centers on a segment of khangiran’s hydrocarbon formations, analyzing reservoir porosity and permeability. among the plethora of available methods, fractal geometry, particularly through the box counting method, proves apt for estimating these properties. by increasing the box size to explore point distribution in the background space, the method calculates fractal dimensions, aiding in porosity and permeability estimation. applied in modeling, this technique presents a new ellipsoid-based prediction model, providing a comprehensive description of petrophysical properties in reservoir-prone areas. the results, aligned with geological features, mud loss data, and production outcomes, demonstrate remarkable compatibility with lower uncertainty, presenting a promising avenue for enhanced reservoir characterization and performance optimization. the three-dimensional block model estimations derived from the integrated discrete fracture network (dfn) algorithm with a fractal dimension of complex sequences distribution align with well test analysis and production data results. the iterative application and refinement of the dfn algorithm and fractal dimension modeling process hold potential for further enhancement across the khangiran reservoir or other hydrocarbon fields. the findings indicate that well 11 is optimally configured and likely exhibits superior performance in terms of hydrocarbon production within the reservoir.

combining voronoi triangulation discrete fracture network (dfn) models with fractal dimension analysis of complex sequences for predicting porosity and permeability

optimizing reservoir performance in fractured reservoirs relies heavily on understanding and harnessing fracture connectivity at the reservoir scale. voroni triangulation discrete fracture network (dfn) models offer a unique depiction of fractures and their connectivity compared to other methods. petrophysical property modeling involves various algorithms, with dfn emerging as a novel mathematical approach. this study centers on a segment of khangiran’s hydrocarbon formations, analyzing reservoir porosity and permeability. among the plethora of available methods, fractal geometry, particularly through the box counting method, proves apt for estimating these properties. by increasing the box size to explore point distribution in the background space, the method calculates fractal dimensions, aiding in porosity and permeability estimation. applied in modeling, this technique presents a new ellipsoid-based prediction model, providing a comprehensive description of petrophysical properties in reservoir-prone areas. the results, aligned with geological features, mud loss data, and production outcomes, demonstrate remarkable compatibility with lower uncertainty, presenting a promising avenue for enhanced reservoir characterization and performance optimization. the three-dimensional block model estimations derived from the integrated discrete fracture network (dfn) algorithm with a fractal dimension of complex sequences distribution align with well test analysis and production data results. the iterative application and refinement of the dfn algorithm and fractal dimension modeling process hold potential for further enhancement across the khangiran reservoir or other hydrocarbon fields. the findings indicate that well 11 is optimally configured and likely exhibits superior performance in terms of hydrocarbon production within the reservoir.