Abstract: Process for interpreting one or more borehole images for use in drilling operations. In some embodiments, the process can include providing an input borehole image obtained from a downhole measurement provided by one or more downhole sensors. The process can also include collecting contextual information relative to the borehole image and the user. The process can also include using the collected contextual information and a mathematical model to infer one or more processing arguments. The mathematical model can be defined by using previously collected arguments and previously collected contextual information. The process can also include processing the input borehole image with the one or more inferred processing arguments to generate one or more interpreted borehole images. The process can also include adjusting one or more drilling operation based, at least in part, on the one or more interpreted borehole images.

Abstract: Generating a virtual core includes obtaining acquired data for a region of interest, determining a rock type of the region of interest, obtaining a selection of modules based on the rock type, and generating, using the acquired data and an interpolator from the modules, a wellbore image of the region of interest. The interpolator generates interpolated data between data points among the acquired data in the wellbore image. Further, using a quantifier from the plurality of modules, a core characterization of the region of interest is determined. The core characterization describes an integration of wellbore data types. Using the core characterization and the wellbore image, a digital core construction of the region of interest is generated. The digital core construction describes subterranean formation properties of the region of interest.

Abstract: Using machine learning for sedimentary facies prediction by using one or more logs acquired in a borehole. This includes performing a petrophysical clustering of borehole depths wherein the depths of the borehole are gathered into clusters based on similarities in the one or more logs. Also performed is a log inclusion optimization, including a selection of one or more parameters of the petrophysical clustering, wherein the one or more parameters include a number and/or type of considered logs among the one or more logs and/or a clustering method. Also performed is a classification of the clusters into core depositional facies using one or more predetermined rules.

Abstract: Process for interpreting one or more borehole images for use in drilling operations. In some embodiments, the process can include providing an input borehole image obtained from a downhole measurement provided by one or more downhole sensors. The process can also include collecting contextual information relative to the borehole image and the user. The process can also include using the collected contextual information and a mathematical model to infer one or more processing arguments. The mathematical model can be defined by using previously collected arguments and previously collected contextual information. The process can also include processing the input borehole image with the one or more inferred processing arguments to generate one or more interpreted borehole images. The process can also include adjusting one or more drilling operation based, at least in part, on the one or more interpreted borehole images.

Abstract: Using machine learning for sedimentary facies prediction by using one or more logs acquired in a borehole. This includes performing a petrophysical clustering of borehole depths wherein the depths of the borehole are gathered into clusters based on similarities in the one or more logs. Also performed is a log inclusion optimization, including a selection of one or more parameters of the petrophysical clustering, wherein the one or more parameters include a number and/or type of considered logs among the one or more logs and/or a clustering method. Also performed is a classification of the clusters into core depositional facies using one or more predetermined rules.

Abstract: Irreducible water saturation of fluid-storing porous reservoir rock is determined using methods that include obtaining a reservoir rock sample from the underground fluid reservoir; performing mercury saturation measurements on the reservoir rock sample for different mercury injection pressure values to obtain mercury saturation values for the different mercury injection pressure values; and estimating the irreducible water saturation (Swirr) from the mercury saturation values and the mercury injection pressure values by correcting for surface films of fluid retained on pore walls of the reservoir rock.

Abstract: Generating a virtual core includes obtaining acquired data for a region of interest, determining a rock type of the region of interest, obtaining a selection of modules based on the rock type, and generating, using the acquired data and an interpolator from the modules, a wellbore image of the region of interest. The interpolator generates interpolated data between data points among the acquired data in the wellbore image. Further, using a quantifier from the plurality of modules, a core characterization of the region of interest is determined. The core characterization describes an integration of wellbore data types. Using the core characterization and the wellbore image, a digital core construction of the region of interest is generated. The digital core construction describes subterranean formation properties of the region of interest.

Abstract: Methods and systems acquiring acoustic data utilizing a downhole tool conveyed within a borehole extending into a subterranean formation. The downhole tool is in communication with surface equipment disposed at a wellsite surface from which the borehole extends. Techniques involve operating at least one of the downhole tool and the surface equipment to generate a histogram based on the acoustic data, normalizing the histogram, and calculating a vug index based on the normalized histogram and based on a threshold of the normalized histogram. A vug porosity quantity may be determined based on the calculated vug index.

Abstract: Irreducible water saturation of fluid-storing porous reservoir rock is determined using methods that include obtaining a reservoir rock sample from the underground fluid reservoir; performing mercury saturation measurements on the reservoir rock sample for different mercury injection pressure values to obtain mercury saturation values for the different mercury injection pressure values; and estimating the irreducible water saturation (Swirr) from the mercury saturation values and the mercury injection pressure values by correcting for surface films of fluid retained on pore walls of the reservoir rock.