A total of 130 experiments were conducted on different rock types such as shales, sandstone, tight carbonates, and synthetic samples. In the first part of the study, to measure the breakdown pressures, a comprehensive hydraulic fracturing experimental study was conducted on various rock specimens. Therefore, in this study, different machine learning (ML) models were efficiently utilized to predict the breakdown pressure of tight rocks. Conducting hydraulic fracturing experiments in the laboratory is a very expensive and time-consuming process. To effectively design hydraulic fracturing jobs, accurate values of rock breakdown pressure are needed. The most economical way to produce hydrocarbons from such reservoirs is by creating artificially induced channels. Unconventional oil and gas reservoirs are usually classified by extremely low porosity and permeability values.
