Neural Network and 3D Seismic Techniques Improve the Prediction of Facies Distribution within a Submarine Channel Complex. Neuquén Basin, Argentina

Raggio, Fernanda¹, Anna Ortin², Claudia Martinez³, Pablo Uzzo⁴ (1) REPSOLYPF, 8300 Neuquén, Argentina (2) REPSOLYPF, C1035AEB Buenos Aires, Argentina (3) Schlumberger, C1035AEB Buenos Aires, Argentina (4) Schlumberger, Buenos Aires, Argentina

Los Molles Formation in the Neuquén basin, west-central Argentina, contains a series of deep-water submarine channel complexes deposited in an elongate basin, during the Pliensbachian-Toarcian ages. These events developed during the final stages of the rifting phase. In the studied area, these submarine meandering canyons cut the shelf and gentle slope in a SSE-NNW direction, and represent the transference zone of the system, where erosional features are more frequent and lithological distribution is more complex. The canyons are approximately 3 Km wide and several hundred meters thick and host a number of migrating channel-fill units inside, lying on erosional surfaces that cut into adjacent interchannel facies.

Applying neural network techniques in the two wells that penetrate this deep-marine strata, allowed to identify four main lithofacies: muddy matrix-conglomerates, coarse grain sandstones, fine grain sandstones and mudstones. Furthermore, the use of 3D seismic attributes was crucial to obtain the distribution of these facies within the canyons. For this purpose, techniques based on neural network and representative supervised “seed points” next to each lithology around the well, were applied.

This work resulted in a seismic volume with the distribution of four seismic facies along the system in a very heterogeneous way. The fine grained facies clearly located in an overbank position; the sandstones and conglomerates show a distribution constrained inside the canyons, and is also easy to see how the net-to-gross relationship increase towards the distal positions of the system. The techniques applied, greatly improve the success of prediction of potential reservoirs locations.