While geosteering or interpreting horizontal wells, engineers are constantly faced with the challenge of correctly defining the well position with respect to the target reservoir and other geologic markers from an often incomplete set of data. Traditional petrophysical evaluation can be performed only after the geometric relationship between the well and the target reservoir is interpreted. Measurements made while drilling range from a simple gamma-ray (GR) log to resistivity, apparent density and neutron porosities, borehole images, azimuthal deep resistivity, and beyond. With one or a combination of these logs and other peripheral data such as well logs from offset/pilot wells and seismic images, the question remains as to how accurate the interpreted relationship is between the well trajectory and the target reservoir. In a vertical well, an explicit geometric model of the formation is often not necessary. There is a 1D layered-earth model, either consciously or subconsciously, in people's minds to aid the interpretation. On the other hand, because of the lateral variation of properties and thus the geologic complexity encountered in a horizontal well, it becomes critical to explicitly construct a formation cross-sectional model, validated in terms of its correctness and uniqueness against the available measurements and other known information. To properly construct, update, and validate the formation model, the completeness of information for solving the geometric relationship between the well and the target reservoir is investigated. It is found that a data set with logs from the horizontal well alone is not adequate for the task. Proper constraints based on depositional environments must be introduced. Well logs from offset/pilot wells define the formation sequence and add the high-resolution details to the interpretation. Three-dimensional reservoir models from seismic images guide the interpretation of the geologic trends along the well trajectory and the extrapolation of the model property into the volume that is not sensitive to well logs. Modeling the tool responses and understanding the underlying response characteristics help to mitigate the interpretation uncertainty by extracting more geometric information from the physical measurements. One should also be aware of the possibility that the interpretation might not be unique even though a model fits the data.