Campeche Sound, located southeast of the continental shelf in the Gulf of Mexico. represents about 80% of the national hydrocarbons production of Mexico, and comprises several giant oilfields, including Cantarell and Ku-Zaap-Maloob. The reservoir rock was deposited during the Cretaceous over the Yucatan Slope and is divided into Upper, Middle, and Lower Cretaceous. The main reservoir rocks are carbonate debris flow facies in the Upper Cretaceous. The formation was diagenetically altered by dolomitization, dissolution, and fracturing processes. All these processes were related to a compressional tectonic regime. Dolomitization in this area is a major control on porosity. When dolomitization exists porosity is improved and is divided into three kinds: matrix, fracture and vug porosity. Fracture and vug porosities are the main productive porosities because they increase connectivity among porous voids. Dolomitization is not homogeneous in the Cretaceous rocks in the study area, which is an important difference with the major fields cited above. Dolomitization is present in the upper and middle part of the Upper Cretaceous and in the Middle Cretaceous, but not in the Lower Cretaceous. The lower part of the Upper Cretaceous is not completely dolomitized in the study area. This heterogeneity in the porosity, and consequently in the permeability, could form vertical barriers to the flow, and it could increase the mobility of fluid movement in the aquifer in the zone, creating early irruptions of water during the production of the future wells. To characterize these complex fields and plan their development, I developed an integrated workflow. The ultimate objective of this research was a 3D-cellular model that represented all the geological complexities identified in the fields through well and seismic data. The first part of this workflow described in Chapter 1, is to define the architecture and structure of the fields. The resulting structural model was supported by the interpretation of a 3D depth migrated seismic integrated data with previous studies in nearby fields describing lithofacies and stratigraphical units to subdivide the model based on lithology supported by image well-logs and core reports. In Chapter 2, I focus on the internal distribution of the dolomitized facies in the field. I evaluated different seismic attributes and selected the ones that on both time and depth-migrated best-differentiated dolomite from limestone. Then, I incorporated them into machine learning processes to identify the process that gave us a result that was closer to the expected geology in the area. In Chapter 3, I use Nuclear Magnetic Resonance (NMR) and image logs, I estimated a dual-porosity petrophysical model. This model was then used as a parameter to select a method from those proposed by other authors to estimate dual-porosity based on basic well-logs. The selected method can be applied to future wells in the area. Then, I distributed the petrophysical properties using geostatistical methods based on the lithofacies described in chapter one. I used the dolomitization trends estimated in chapter 2, as a second variable into the geostatistical process. The result was a 3D model, which identified sweet spots to locate new development wells, estimate original volumes and, make simulations of the production of the fields.