The oil and gas industry faces significant challenges when extracting natural gas from deep, tight formations, particularly ultra-low-permeability shale and sandstone reservoirs. Economic production requires hydraulic fracturing, and a robust geomechanical model is essential for optimizing hydraulic fracture design. Traditionally, closure pressure determination involves the Diagnostic Fracture Injection Test (DFIT) and G-function analyses, which require predefined values for instantaneous shut-in pressure (ISIP) and introduce uncertainties. This thesis addresses the critical challenge of estimating closure pressure in deep tight gas reservoirs, specifically those within the depth range of 4000 to 5000 meters, located in North Oman. Methodology initially involves conducting three DFIT tests and performing Gfunction analysis, followed by wavelet analysis. By decomposing the pressure signal into different levels, we analyze the noise of the signal over time. Level 6 is identified as suitable for analysis, with the energy distribution of the pressure signal showing decreasing levels of noise energy, eventually dropping to a minimum level at closure. Closure pressure is then determined based on this zero-strength point. Comparative analysis with the G-function method aligns well for three datasets, and further testing across four additional DFITs cases from different fields yields corroborative results, showcasing the effectiveness and reliability of the proposed wavelet-based approach. The main advantage of our approach lies in its lack of pre-assumptions about fracture geometry or well type, relying solely on recorded pressure signals during the falloff period. This unique feature extends its applicability across various formations, rocks, and well types, making it a versatile tool for optimizing fracture design and enhancing hydrocarbon recovery.