Middle East brown fields are penetrated by, more or less, around 1000 wells with long-production history. Attempts to incorporate all these wells create huge challenge caused by uncertainty related to well data discrepancy. The discrepancy in acquisition methods, tools, vintages are few factors to name that are root causes resulting in different depth values. Therefore, varying depth makes difficult to build structure of 3D reservoir model without non-geological anomalies such as distorted, collapsed, non-orthogonal cells. Methodology is to introduce uncertainty to all data feeding the structure modelling process. Following data is used with their ranges of uncertainties: interval velocity, seismic time maps, thickness maps, geological markers and well surveys. These ranges have to be identified quantitatively as they will give us flexibility to integrate data in the model within justifiable windows. Initially, the maps are allowed to change and try to integrate all data without well survey modifications. If, even after number of iterations, data is still not consistent resulting non-geological anomalies, then it is good to try allowing survey to change, but cautiously. Following application of this workflow, the data started to come in agreement and resulted in smooth, geologically reasonable subsurface structures. Horizontal wells targeting multiple thin carbonates are the most challenging to place them correctly. These wells require a lot of iterations or manual intervention to incorporate in the model, sometimes by adding number of pseudo-wells. Figure 1 shows even those examples can result in geological markers, of both vertical and deviated sections, match structure surfaces where horizontal trajectory at right penetrated layers. Worth to mention that the integration of almost ~1000 wells required zero pseudo-wells that helps to avoid introducing unrealistic noise to the data. Successful implementation of this project made this giant field one of the first brownfields that incorporated all data in consistent manner without using pseudo-wells. This structure model will maximize the value from ADNOC's existing data resources to reduce uncertainties during subsequent property and dynamic modelling stages plus while drilling future wells. Average estimates show that proper integration of all data can bring minimum $18 million in value.