Abstract: Methods and systems are provided that predict electromagnetic properties of drilling mud and a formation, which involve a logging tool that measures current injected into a measurement zone adjacent a sensor electrode at multiple frequencies. The measured currents at the multiple frequencies are processed to determine complex impedances for the sensor electrode at the multiple frequencies. The complex impedances are used to generate input data, which is supplied to a system of artificial neural networks (ANNs) that is configured to predict and output electromagnetic properties of the drilling mud and the formation within the measurement zone and possibly tool standoff based on the input data. The system of ANNs can employ a cascaded architecture of multiple ANNs. The electromagnetic properties or tool standoff predicted by the system of ANNs can be used to construct a borehole image over varying azimuth and depth.

Abstract: Embodiments of the present disclosure are directed towards systems and methods for automated stratigraphy interpretation from borehole images. Embodiments may include constructing, using at least one processor, a training set of synthetic images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images. Embodiments may further include automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or machine learning techniques. Embodiments may also include automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

Abstract: Methods and platforms for allowing efficient identification of 3D stratigraphic models that explain observed log data.

Abstract: Aspects of the present disclosure relate to a well analog recommendation system. The well analog recommendation system may generate numerical representations indicative of text-based descriptions within a well report and/or well log associated with a well. Further, the well analog recommendation system may generate a well analog output that may include one or more text-based characterizations associated with one or more additional wells that are determined based on the numerical representation. For example, the well analog recommendation system may compare the numerical representation of the well to one or more numerical representations associated with the one or more additional wells and output the one or more text-based characterizations when the numerical representations are approximately equal or above a threshold.

Abstract: Embodiments of the present disclosure are directed towards systems and methods for automated stratigraphy interpretation from borehole images. Embodiments may include constructing, using at least one processor, a training set of synthetic images corresponding to a borehole, wherein the training set includes one or more of synthetic images, real images, and modified images. Embodiments may further include automatically classifying, using the at least one processor, the training set into one or more individual sedimentary geometries using one or machine learning techniques. Embodiments may also include automatically classifying, using the at least one processor, the training set into one or more priors for depositional environments.

Abstract: Methods and systems are provided that predict electromagnetic properties of drilling mud and a formation, which involve a logging tool that measures current injected into a measurement zone adjacent a sensor electrode at multiple frequencies. The measured currents at the multiple frequencies are processed to determine complex impedances for the sensor electrode at the multiple frequencies. The complex impedances are used to generate input data, which is supplied to a system of artificial neural networks (ANNs) that is configured to predict and output electromagnetic properties of the drilling mud and the formation within the measurement zone and possibly tool standoff based on the input data. The system of ANNs can employ a cascaded architecture of multiple ANNs. The electromagnetic properties or tool standoff predicted by the system of ANNs can be used to construct a borehole image over varying azimuth and depth.