Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.     

Local and controlled prestack depth migration in complex areas

Prestack depth migrations based on wavefield extrapolation may be computationally expensive, especially in 3D. They are also very dependent on the acquisition geometry and are not flexible regarding the geometry of the imaging zone. Moreover, they do not deal with all types of wave, considering only primary reflection events through the model. Integral approaches using precalculated Green's functions, such as Kirchhoff migration and Born-based imaging, may overcome these problems. In the present paper, both finite-difference traveltimes and wavefront construction are used to obtain asymptotic Green's functions, and a generalized diffraction tomography is applied as an example of Born-based acoustic imaging. Target-orientated imaging is easy to perform, from any type of survey and subselection of shot/receiver pairs. Multifield imaging is possible using Green's functions that take into account, for instance, reflections at model boundaries. This may help to recover parts of complex structures which would be missing using a paraxial wave equation approach. Finally, a numerical evaluation of the resolution, or point-spread, function at any point of the depth-migrated section provides valuable information, either at the survey planning stage or for the interpretation.     

Regional geochemical reconnaissance in the Belgian Ardennes: Secondary dispersion patterns in stream sediments

Two geochemical stream sediment surveys were conducted in the Belgian Ardennes for Fe, Cu, Pb, Zn, Co, Ni and Mn. The geological setting is of quartzo-pelitic rocks of Early Devonian and Cambro-Ordovician age. One survey covers the Ourthe and Salm valleys, the other the Lesse and Lomme drainage basins. Results of these surveys show the geochemical similarity of the two regions. Anomalies have been ascribed to four factors: human activity, Fe-Mn enrichment, known mineralization and potential mineralization. Mean values of metal concentration and statistical dispersion are identical. This results from similar physico-chemical conditions which induce the same types of surficial metallic association. The stream environment is characterized by two main phases, an oxidizing phase where Fe-Mn oxides trap trace metals, and a reducing phase where organic matter and secondary sulphides are bound with Pb, Zn and Cu. In oxidizing conditions, Cu and Ni show a greater affinity for the Fe-oxides while Zn and especially Co are related to the Mn-oxides. Pb has only a weak relationship with the Fe-Mn oxides but is also associated with the organic matter and the secondary sulphides in the reducing environment. Consequently, one may propose the following scale of mobility: Zn > Ni-Mn > Cu-Co > Pb > Fe.     

Climate change and recent water level variability in Patagonian proglacial lakes, Argentina

A long series of lakes (~ 150) borders the Patagonian Andes (south of ~ 38°S), most of which are a geomorphologic relict of Pleistocene glaciations. Employing instrumental records, we inspected lake water level departures from seasonal variations in seven proglacial lakes: Lacar, Mascardi, Steffen, Escondido, Puelo, Vinter, and Argentino. Lakes north of ~ 42°S show maximum gage (water) level during austral winter months; lakes between ~ 42° and ~ 45°S appear transitional; the one lake south of ~ 50°S (Argentino) shows maximum water level in early autumn. Most lakes show moderate level fluctuation throughout yearly records and, in general, show heteroscedacity. Furthermore, Hurst exponents reveal persistent behavior (i.e., long-term memory effect) in all water level series. In most lakes there are no trends in deseasonalized mean and maximum water levels (Seasonal Kendall test). Lake Mascardi–Manso River system (mostly fed by melt water from the retreating Manso Glacier) is a contrasting example that shows a decreasing trend during summer months that we ascribe to the also declining ice volume. Harmonic analysis (Fourier and wavelet transform) of deseasonalized mean and maximum water level time series shows interannual and decadal periodicities that we link to the occurrence of El Niño and/or the Antarctic Oscillation. The associated phase spectrum indicates that there is a ~ 13-month lag between ENSO occurrences and its effect on anomalous lake water levels. Increased snow accumulation during austral winters usually follows summertime El Niño events, which normally result in increased melt water volume that occurs with about one-year delay during the following (austral) spring/summer.     

Influence of geomorphological variables on mountainous stream water chemistry (Sierras Pampeanas, Córdoba, Argentina)

The relationship between geomorphological features and water geochemistry was studied for a group of mountainous rivers (from ~ 900 to ~ 2200 m a.s.l.) with similar geology and climate, in the Sierras Pampeanas of Córdoba (Argentina, 31° 30′, 32° 00′S, and 64° 30′, 65° 10′W). A multivariate approach was used to identify three morphological domains that describe the set of sampled rivers, namely “size” dominance, slope dominance, and drainage density dominance. The links between physicochemical and geomorphological variables show that “size” dominance is mostly related to major ions, conductivity, and pH, which tend to increase downstream. Slope dominance is associated with the total concentration of heavy metal (i.e., high heavy metal concentrations are associated with relatively flat areas with slightly acid to circumneutral pH, which promotes desorption). The drainage density dominance results in an association between well-drained catchments and low Cl⁻ concentration (i.e., preserving the chemical signature of atmospheric precipitation).