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Modeling of channel stacking patterns controlled by near wellbore modeling


Reservoir models of deep-water channels rely upon low-resolution but spatially extensive seismic data, high vertical resolution but spatially sparse well log data and geomodeling methods. The results cannot predict architecture below seismic resolution or between well logs. Usually, the data and interpretations that provide constraints for modeling workflows do not capture sub-seismic scale architecture. Therefore, standard modeling methods do not generate models that include details that can impact hydrocarbon flow and recovery. Constraining models to well and seismic data is problematic. Employing measured sections in the Tres Pasos Fm. (Magallanes Basin, Chile) is feasible to predict deep-water channel architecture, specifically channel stacking patterns with 1D information analogous to well data. This research performed near-wellbore modeling to generate multiple scenarios of channel stacking patterns constrained by machine learning-derived probabilities using (i) conditional Monte Carlo simulation with soft probabilities per channel element within the measured section choosing the highest probabilities for each element (ii) conditional Monte Carlo simulation of channel stacking, (iii) template-based modeling, (iv) forward modeling with Markov transition probabilities without matching to thickness and (v) conditional Monte Carlo simulation constrained to measured section thickness. Machine learning workflows generate channel position probabilities (i.e., axis, off-axis, margin) within a measured section given the interpreted top/bases of channel elements. These probabilities constitute the input for Monte Carlo simulations capturing channel element stacking patterns at the measured section locations. The most likely 2D channel stacking pattern scenarios defined channel centerline points, and volumes of the individual channel elements can be generated connecting them. Surface-based modeling offers a way to depict reservoirs of hydrocarbons, water or low-enthalpy geothermal systems in which small-scale heterogeneity needs to be captured explicitly by bounding surfaces because it impacts fluid flow, improving our forecasts of resource exploitation. Furthermore, predicting heterogeneity controlled by depositional architecture is critical for transport and storage capacity in CO2 reservoirs. The dataset provided and the advent of these flexible and accurate methods to depict the subsurface offer the opportunity to overcome the historical limitations of grid-based models and allow us to assess multi-scale architecture that controls fluid flow. This research aims to show the results of modeling deep-water channels, including a 1D identification of architectural positions and a 2D arrangement of channel stacking patterns.


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deep-water channels
Markov transition probabilities
channel stacking patterns


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