Abstract: Methods and systems are provided for characterizing connate water salinity and resistivity of a subsurface formation. Well log data including resistivity and spontaneous potential (SP) log data are measured by at least one downhole tool disposed within a borehole. The resistivity and SP log data are inverted to determine a resistivity model and an SP model, which are used to determine connate water resistivity. The connate water resistivity is used to determine connate water salinity. The connate water salinity derived from the inversion of resistivity log data and SP log data (or derived from a trained ML system supplied with such log data) can be used as a baseline measure of connate water salinity, and this baseline measure can be evaluated together with the connate water salinity estimates derived from pulsed neutron tool measurements over time-lapsed periods of production to monitor variation in connate water salinity due to production.

Abstract: A method can include receiving Stoneley wave data acquired by a sonic tool disposed in a borehole in a formation; performing an inversion using the Stoneley wave data to generate formation and borehole information; and identifying an interval along the borehole for performance of a downhole acquisition operation by a downhole tool using a machine learning model and the formation and borehole information as input to the machine learning model.

Abstract: A method for characterizing organic matter in a geological rock formation includes performing a Raman spectroscopy measurement on a rock sample to acquire a Raman spectrum of the rock sample. The method includes analyzing the Raman spectrum of the rock sample to determine data characterizing at least one Raman spectral feature corresponding to the rock sample, inputting the data characterizing the at least one Raman spectral feature corresponding to the rock sample into a computation model that determines a value of a property of organic matter in the rock sample, and storing or outputting or displaying the value of the property of organic matter in the rock sample. In embodiments, the property of organic material in the rock sample can be a property of kerogen in the rock sample, or a property of asphaltenes or bitumen in the rock sample.

Abstract: Systems and methods are described for correcting ambient condition capillary pressure curves. In an example, the capillary pressure curve of a porous medium can be measured in non-reservoir conditions. Samples of the porous medium can be scanned at various confining pressures, and digital models of the scans can be created by a computing device. The computing device can simulate a porous plate experiment on the digital models to create a capillary pressure curve for each model. The computing device can calculate fitting equations for each capillary pressure curve according to capillary pressure points. The fitting equations can be averaged together and applied to the capillary pressure curve of the porous medium to correct for the non-reservoir conditions.

Abstract: Processes for characterizing reservoir formations and directing downhole operations based on the characterized reservoir formations. In some embodiments, the process can include combining at least two downhole logs into input data. An interpretation method can be used to convert the input data into interpretative data. Elemental analysis can be used to convert the interpretative data into at least one formation model. Formation properties can be acquired from the at least one formation model. A reservoir quality classification can be created from the formation properties. The process can also include directing downhole operations using the reservoir quality classification to select a preferred downhole operation location.

Abstract: The present disclosure is directed to an improved process that measures IFT of petroleum reservoir fluids (i.e., crude oil) using Fourier-Transform Infrared Spectroscopy (FTIR) measurements. FTIR spectra of a petroleum reservoir fluid sample are measured and processed to generate FTIR data that characterizes or accounts for the surface-active species of the petroleum reservoir fluid sample. The resulting FTIR data is input to a predefined correlation function that calculates IFT of the petroleum reservoir fluid sample given the FTIR data. This new technique helps minimize the time for experiment preparation and stabilization.

Abstract: Methods and systems are provided for characterizing connate water salinity and resistivity of a subsurface formation. Well log data including resistivity and spontaneous potential (SP) log data are measured by at least one downhole tool disposed within a borehole. The resistivity and SP log data are inverted to determine a resistivity model and an SP model, which are used to determine connate water resistivity. The connate water resistivity is used to determine connate water salinity. The connate water salinity derived from the inversion of resistivity log data and SP log data (or derived from a trained ML system supplied with such log data) can be used as a baseline measure of connate water salinity, and this baseline measure can be evaluated together with the connate water salinity estimates derived from pulsed neutron tool measurements over time-lapsed periods of production to monitor variation in connate water salinity due to production.

Abstract: Process for determining rock permeability. In some embodiments, the process can include determining a volume-based aspect ratio distribution of pores in a rock sample from a digital image of the sample, grouping the volume-based aspect ratio distribution into two or more pore types, selecting an initial pore type from the two or more pore types, obtaining mercury injection capillary pressure (MICP) data of the sample, creating a volume forward model and a frequency forward model using the MICP data, deriving an initial volume-based pore size distribution and an initial frequency-based pore size distribution for the initial pore type using the volume and the frequency forward models, respectively, selecting either the initial volume-based or the initial frequency-based distribution based on the forward models, and optimizing the selected distribution using an inversion of the MICP data with combinations of two or more pore type distributions to create an optimized distribution.

Abstract: A method can include receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; processing the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resampling the smoothed time series data; generating fluid and formation characteristics with respect to time based on the smoothed time series data; and outputting the fluid and formation characteristics with respect to time.

Abstract: A method can include receiving petrophysics data acquired along a borehole in a subsurface region; generating test location recommendations along the borehole using the petrophysics data as input to a machine learning model; and outputting, based on the test location recommendations, selected locations for performing tests using a downhole tool disposed in the borehole

Abstract: Methods and systems are provided that use resistivity log data to estimate water saturation of formation rock and/or other useful formation parameters (such as CEC) in a manner that accounts for one or more electrically conductive mineral components contained in the formation rock.

Abstract: Systems and methods are described for locating hydrocarbons near a wellbore, determining petrochemical properties of a reservoir, and assessing hydrocarbon saturation between reservoirs. The nanoparticles can be positively charged and have properties that make them explode upon contacting crude oil. Sensors can be strategically placed near the wellbore to measure micro tremors caused by nanoparticle explosions. When the fluid is pumped into the wellbore, the fluid is forced into the surrounding rock formations. The fluid can contact crude oil in the rock formations, and the nanoparticles can attract to the fluid/oil interface due to negatively charged particles in the crude oil. When the nanoparticles reach the fluid/oil interface, they can contact the crude oil and explode. The seismic sensors can detect micro tremors caused by the nanoparticle explosions. The seismic sensor readings can be superimposed to identify the location of the crude oil and assess hydrocarbon saturation.

Abstract: A method for characterizing a subterranean formation is provided that involves obtaining resistivity log data measured by a logging-while-drilling electromagnetic tool operated while drilling a wellbore that traverses the subterranean formation, and calculating at least one of a first data value representing water saturation of the subterranean formation and a second data value representing cation exchange capacity (CEC) of the subterranean formation from the resistivity log data. In embodiments, the method can further involve storing at least one of the first data value and the second data value in computer memory, and/or outputting at least one of the first data value and the second data value as part of a log for well placement, formation evaluation. geological modeling, or reservoir management. The first data value and/or the second data value can also be used for geo-steering the drilling of the wellbore.

Abstract: A workflow is provided that extracts and isolates an oil/water interface of a formation fluid sample and employs a ToF-SIMS instrument to characterize properties of the oil-water interface of the formation fluid sample. Additionally or alternatively, the workflow can use a ToF-SIMS instrument to analyze a formation rock sample and characterize properties of the formation rock sample. The workflow can also involve combining the at least one property related to the oil-water interface of the formation fluid sample and the least one property related to the formation rock sample for output or display to a user.

Abstract: Methods and systems are provided for predicting thermal properties of a subsurface rock formation. A training dataset is derived from petrophysical properties of a plurality of formation rock samples and thermal properties of the plurality of formation rock samples. The training dataset is used to train a machine learning model that predicts label data representing the predefined set of thermal properties given input data representing the predefined set of petrophysical properties of an arbitrary formation rock sample. The machine learning model can be validated and deployed for use in predicting thermal properties of subsurface rock formations.

Abstract: A method can include receiving Stoneley wave data acquired by a sonic tool disposed in a borehole in a formation; performing an inversion using the Stoneley wave data to generate formation and borehole information; and identifying an interval along the borehole for performance of a downhole acquisition operation by a downhole tool using a machine learning model and the formation and borehole information as input to the machine learning model.

Abstract: A method may include acquiring NMR data and sonic data for a borehole in a subsurface geologic region; inverting the NMR data and the sonic data to determine volume fractions for a number of classes of pore types, where the classes include shape and size-based classes; and characterizing the subsurface geologic region based on the volume fractions for the number of classes.

Abstract: Methods and systems are provided for clay detection, clay typing, and clay volume quantification using downhole electromagnetic measurements conducted by a downhole logging tool on a formation at a low frequency less than 5000 Hz. The downhole electromagnetic measurements are used to determine permittivity data that characterizes permittivity of the formation at the low frequency less than 5000 Hz. The downhole low frequency electromagnetic measurements are nondestructive, and the results indicate it is with high sensitivity to the existence of clays.

Abstract: Methods and systems are provided that perform sonic measurements in a high-angle wellbore or horizontal wellbore or vertical wellbore penetrating highly dipped formation layers where the formation layers can have a high degree of dip relative to the wellbore. Sonic data can be generated from the sonic measurements and processed using multiple arrival event processing to determine formation porosity, elastic rock properties and geometric information for a tool layer and nearby shoulder bed. Such information can be integrated into a 2D or 3D layered model of the formation. The elastic rock properties of the tool layer and shoulder bed derived from the multiple arrival event processing can provide more representative elastic property values, which can account for heterogeneity along the wellbore.

Abstract: Methods and systems are provided that determine or estimate total clay volume fraction in a formation for well log data of the formation using machine learning are described. Methods and systems are also provided that employ computation models to determine amounts or concentrations of at least one clay mineral in the formation from total clay volume fraction of the formation.