Target zone selection for hydraulic fracturing using sedimentological and rock mechanical studies with the support of the machine learning method of cluster analysis

Abstract

Appropriate zone selection is crucial for hydraulic fracturing as it can significantly impact the success of the treatment and the overall cost. Many parameters affect true zone selection for hydraulic fracturing but lithology and geomechanical parameters are the most important. In this study, geological and geomechanical studies were incorporated with statistical approaches to determine geomechanical zones and intervals, which are brittle and can be candidates for hydraulic fracturing for the Sarchahan, Zakeen, and Faraghan formations (Silurian to Permian) in the deepest well in southern Iran Offshore. Based on sedimentological studies, five sedimentary facies were recognized: Sandstone, Shale, Red Mudstone, Mudstone, and Carbonate. In the next step, rock mechanical parameters were extracted from petrophysical logs and experimental tests. Six Rock Mechanical Facies (RMF) were determined based on main rock mechanical parameters including Young's modulus (E), Poisson's ratio (PR), uniaxial compressional strength, internal friction, and cohesion along with statistical clustering approaches. Then, nine geomechanical units (GMU), including GMU A to GMU I, were extracted from RMFs and were correlated with geological studies. Brittleness index (BI) was calculated based on E and PR and was correlated with variation of Al2O3 and SiO2 from XRF analyses. Brittleness index, GMUs rock mechanical properties, homogeneity of GMUs, and their geological characteristics are used to determine possible candidate GMUs for hydraulic fracturing. Three GMUs are recognized as the best GMUs for hydraulic fracturing in the studied well.