Abstract: Embodiments herein include a system and method for modeling and interpreting an evolution of fluids in an oilfield using artificial intelligence. Embodiments may include identifying, using at least one processor, one or more reservoir fluid dynamics processes or properties and generating a model for the one or more reservoir fluid dynamics processes or properties. Embodiments may include receiving, at the model, one or more parameter values corresponding to the one or more reservoir fluid dynamics processes or properties and displaying, at a graphical user interface, one or more results, based upon, at least in part, the model and the one or more parameter values.

Abstract: Methods and systems are provided to learn and apply a mapping function from data representing concentrations of atomic elements in a geological formation (or other data corresponding thereto) to mineral component concentrations in the geological formation (and/or from mineral component concentrations to reconstructed elemental concentrations in the geological formation). The mapping function can be derived from a trained neural network (such as an autoencoder). The output of the mapping function can be used to determine estimates of one or more formation properties, such as formation matrix density, formation porosity, matrix Sigma, formation saturation, other formation property, or combinations thereof.

Abstract: Methods and systems are provided characterizing a formation traversed by a wellbore, wherein the formation includes at least a flushed zone and an uninvaded zone, which involve obtaining well log data based on plurality of different well log measurements of the formation at multiple depths in the wellbore. The well log data is used to a computational model that solves for a set of petrophysical parameters that characterize a portion of the formation corresponding to the multiple depths in the wellbore, wherein the set of petrophysical parameters include a cementation exponent, a saturation exponent, and a flushed zone water resistivity. The solved-for set of petrophysical parameters can be used to determine a value of water saturation of the uninvaded zone for the portion of the formation corresponding to the multiple depths in the wellbore.

Abstract: A nuclear magnetic resonance (NMR) measurement, at two or more depths of investigation, of a subsurface formation containing formation water and a mud filtrate from a water-base mud is obtained, and the mud filtrate is distinguished from the formation water. A NMR logging tool is disposed in a wellbore penetrating the formation containing the mud filtrate and the formation water and NMR measurements at different radial depths of investigation into the formation are made. The mud filtrate is distinguished from the formation water by determining the relative salinities of the mud filtrate and the formation water. The relative salinities are determined by comparing distribution relaxation times across different depths of investigation or by comparing logarithmic mean values across different depths of investigation.

Abstract: A nuclear magnetic resonance (NMR) measurement, at two or more depths of investigation, of a subsurface formation containing formation water and a mud filtrate from a water-base mud is obtained, and the mud filtrate is distinguished from the formation water. A NMR logging tool is disposed in a wellbore penetrating the formation containing the mud filtrate and the formation water and NMR measurements at different radial depths of investigation into the formation are made. The mud filtrate is distinguished from the formation water by determining the relative salinities of the mud filtrate and the formation water. The relative salinities are determined by comparing distribution relaxation times across different depths of investigation or by comparing logarithmic mean values across different depths of investigation.