[Editor's note: A version of this story appears in the June 2020 edition of E&P. Subscribe to the magazine here. It was originally published June 1, 2020.]  

The tectonics of Argentina’s Neuquén Basin has been studied in detail by many authors. The extensional period between the Upper Triassic and Lower Jurassic resulted in a system of northwest-to-southeast-oriented half grabens; and a later compressive-transgressive period from the Upper Jurassic resulted in a system of mainly east-to-west reverse and transgressive faults. Trans-tension related to this period adds structural complexity to the area. Similarly, the stratigraphy and facies of the Vaca Muerta Formation have been studied in detail.

The mega-merged 3D seismic clearly shows the characteristics and geometries that make the interpreta­tion of strata stacking patterns, their stratigraphic rela­tions and the interpretation of internal configurations (e.g., onlaps and downlaps) much easier than before. From the southeast to northwest, it can be observed that the major architectural trends from proximal basin margin facies through central basin settings and into distal basin floor facies (Figure 1).

A total of 14 wells with a relatively complete log set were identified for the petrophysical analysis. Given the complex mineralogy of the Vaca Muerta Formation, a statistical min­eral analysis was used. The method included log response equations for several tools simultaneously to obtain the mineralogy, fluid content and total porosity. Gamma ray, density, neutron and compressional sonic and clay volume estimated from normalized gamma ray were used as input curves for all the wells analyzed. The results show the Vaca Muerta is predominantly limestone, with some marl or siltstone interbeds. Kerogen is mainly deposited in the lower Vaca Muerta formation with a maximum 12% of vol­ume. Total porosity can reach up to 25% at the lower Vaca Muerta. The interpretation was calibrated to core data.

Post-stack seismic inversion

Three key horizons around the target formation, Top B, Vaca Muerta and Todrillo, were interpreted for well tie, background model building and inversion. The seismic data quality is relatively good in the target zone (Vaca Muerta to Todrillo). However, this seismic volume comes from different surveys, and the spectra of differ­ent surveys are not quite balanced, which may affect the inversion results.

All the wells inside the seismic survey are tied with seismic before inversion. The Gardner equation is used to predict the density log for the wells without the den­sity measurement.

The inversion results show a relatively good match with the wells. The lower Vaca Muerta shows relatively low acous­tic impedance (AI), which is a typical Kerogen response. The inverted AI volume, together with the well logs inter­pretation results are then later integrated using the geologic modeling technique for detailed rock property estimation.


A geocellular model represents the reservoir geology and the vertical and horizontal variation of the reservoirs in detailed log scale. The model incorporates the 3D structural framework, the conceptual depositional sys­tem (facies) and reservoir sublayers. The reservoirs and formations above and below the reservoirs, constraining the structural model, are divided first into zones and later into thin layers and into a 3D mesh of cells until the cells with thickness of rocks can simulate the reservoir properties. For each cell, just one lithofacies type can be assigned, but it can storage multiple reservoir properties.

The cells cut by wells are populated with facies and reservoir properties, using averaging methods such as arithmetic mean. The values of facies and a cell’s proper­ties loaded from well logs are distributed or interpolated through all the cells of the geocellular model based on the spatial continuity concepts. The spatial distribution is carried out by applying geostatistics, which includes data analysis and deterministic and stochastic algorithms. Sequential Gaussian Simulation is applied to create heterogeneity-property volumes honoring input/output of local data, histograms and variograms. Rock physics constraints and a co-located cokriging method were used to honor the well data and seismic inversion result simul­taneously. Finally, the geocellular model was populated with petrophysical and elastic rock properties that can be used to assess, develop and manage the reservoir.

Two types of geomodels are created: one using well data only, and the other using both seismic and well data. By comparing two geomodels, significant values of applying seismic data in delineating the 3D rock properties are demonstrated. From the basin-scale geo­models, it was observed that the highest total organic rich and high-porosity zone is located in the lower Vaca Muerta of the southeastern part of the basin.


An integrated study of the Vaca Muerta Formation, Neuquén Basin, was performed using mega-merged 3D seismic data covering about 12,000 sq km to estimate the kerogen, porosity and lithology (Figure 2). Four key tasks were included in this project: regional structural and stratigraphic interpretation, petrophysical analysis, post-stack inversion, and geocellular model building. 3D structures of the Vaca Muerta are revealed from the mega 3D interpretation. Regional scale stratigraphic fea­tures and lateral transitions have been evidenced from a single seismic volume for the first time.

Sesimic survey overlay
FIGURE 2. Seismic surveys overlay a topographic map in which the red dots are wells used in performing well tie and velocity model building for time to depth conversion. (Source: ION Geophysical)

Geocellular modeling combines the well log data and inverted AI volume together with the geological information to produce a detailed description of the geological deposition and kerogen distribution. The basin-scale geocellular model provides a powerful tool for sweetspot identification during the exploration phase and reserve estimation and well planning during field development.