The Groundwater is a resource of high economic, strategic, and social value, whose
sustainable use requires extensive knowledge. The Alter do Chão Aquifer, source of
groundwater in Manaus (AM), represents the saturated portion of the Alter do Chão
formation, being the main water reservoir of the Amazon Basin and one of the most
important aquifers of Brazil. However, information about the aquifer do not show clear
spatial connotations when treated in 3 dimensions, so it is necessary subsurface data and
spatial representation methods - such as the generation of 3D block models from
geological modeling – to provide more data from the aquifer characteristics. In this sense,
to acquire data with greater viability, the well logging is an alternative which estimates
diferente layers resulted from interpretation of profiles that quantify the physical
characteristics of the rocks. In this study, fifteen wells with Gamma Ray (RG),
Spontaneous Potential (SP) and Resistivity (RE) profiles were used, interpreted from
sandstone and clay lithologies collected in gutter samples. For the geological modeling,
the Datamine Studio RM software was used, in which the interpreted data of the profiles
are inserted in a database, and after validations and statistical treatments they are used for
the construction of wireframes, prototype delimitation and blocking. The generated model
represents 26.96 km3 of rocks (77.6% sandstone and 22.4% claystone) and is composed
by 48 blocks in the X direction, 40 in the Y direction and 66 in the Z direction, totaling
126,720 parent cells, each with dimensions of 250 x 250 x 5 m, considered to have the
best adherence for structuring the unit's lenses. The profiles visualization and the
definition of 3 lithological zones (called superior, intermediate, and inferior) was
considered na accurate method in the evaluation of the Alter do Chão aquifer boundaries,
being compatible with the results obtained by 2D mappings and numerical modeling.
With regard to integrity, the insertion of structural data, the use of more wells, in a grid
with greater regularity, and the availability of hardware with greater processing capacity
for microlayer modeling, also tend to increasingly increase the degree of confidence of
3D model.
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