This paper presents an innovative and practical workflow framework implemented in an Oman southern asset. The asset consists of three isolated accumulations or fields or structures that differ in rock and fluid properties. Each structure has multiple stacked members of Gharif and Alkhlata formations. Oil production started in 1986, with more than 60 commingling wells. The accumulations are not only structurally and stratigraphically complicated but also dynamically complex with numerous input uncertainties. It was impossible to assist the history matching process using a modern optimization-based technique due to the structural complexities of the reservoirs and magnitudes of the uncertain parameters. A structured history-matching approach, Stratigraphic Method (SM), was adopted and guided by suitable subsurface physics by adjusting multi-uncertain parameters simultaneously within the uncertainty envelope to mimic the model response. An essential step in this method is the preliminary analysis, which involved integrating various geological and engineering data to understand the reservoir behavior and the physics controlling the reservoir dynamics. The first step in history-matching these models was to adjust the critical water saturation to correct the numerical water production by honoring the capillary-gravity equilibrium and reservoir fluid flow dynamics. The significance of adjusting the critical water saturation before modifying other parameters and the causes of this numerical water production is discussed. Subsequently, the other major uncertain parameters were identified and modified, while a localized adjustment was avoided except in two wells. This local change was guided by a streamlined technique to ensure minimal model modification and retain geological realism. Overall, acceptable model calibration results were achieved. The history-matching framework's novelty is how the numerical water production was controlled above the transition zone and how the reservoir dynamics were understood from the limited data.