Methods and systems are provided that combine NMR and IR spectroscopy measurements on a rock sample to determine data representing at least one property of the rock sample. In one embodiment, cuttings can be split into first and second lots. Results of an NMR measurement performed on the first lot of cuttings without cleaning can be analyzed to determine pore volume of the cuttings. Results of an IR spectroscopy measurement performed on the second lot of cuttings after solvent cleaning can be analyzed to determine matrix density of the cuttings. Porosity can be determined from the pore volume and matrix density of the cuttings. In another embodiment, combined NMR and IR spectroscopy measurements can be performed on an unprepared rock sample (without solvent cleaning) to characterize properties of kerogen in the rock sample and porosity. In another aspect, a method is provided that employs multi-nucleic NMR measurements to determine porosity.

Methods and systems are provided that combine NMR and IR spectroscopy measurements on a rock sample to determine data representing at least one property of the rock sample. In one embodiment, cuttings can be split into first and second lots. Results of an NMR measurement performed on the first lot of cuttings without cleaning can be analyzed to determine pore volume of the cuttings. Results of an IR spectroscopy measurement performed on the second lot of cuttings after solvent cleaning can be analyzed to determine matrix density of the cuttings. Porosity can be determined from the pore volume and matrix density of the cuttings. In another embodiment, combined NMR and IR spectroscopy measurements can be performed on an unprepared rock sample (without solvent cleaning) to characterize properties of kerogen in the rock sample and porosity. In another aspect, a method is provided that employs multi-nucleic NMR measurements to determine porosity.

Provided herein are new methods for assessing carbon dioxide rock volatile contents of subterranean sites to identify changes in the carbon dioxide content of sites, such an assessment then applied to the evaluation of such sites for possible use as carbon capture and sequestration (CCS) sites, carbon capture, utilization, and sequestration (CCUS) sites, and for other applications. Further provided herein are methods for identifying geological features which may compromise a site's ability to serve as a CCS/CCUS site, whereby the methods comprise the application of rock volatiles analysis, mechanical strength analysis, or both, and the subsequent identification of such geological features based on shifts, changes, or patterns in data resulting from the analysis. Additional related methods and systems are also disclosed.

Provided herein are new methods for assessing carbon dioxide rock volatile contents of subterranean sites to identify changes in the carbon dioxide content of sites, such an assessment then applied to the evaluation of such sites for possible use as carbon capture and sequestration (CCS) sites, carbon capture, utilization, and sequestration (CCUS) sites, and for other applications. Further provided herein are methods for identifying geological features which may compromise a site's ability to serve as a CCS/CCUS site, whereby the methods comprise the application of rock volatiles analysis, mechanical strength analysis, or both, and the subsequent identification of such geological features based on shifts, changes, or patterns in data resulting from the analysis. Additional related methods and systems are also disclosed.

A method for training a model. The method may include forming a data set from one or more measurements of core samples, selecting one or more parameters from the data set, inputting the one or more parameters into a kernel estimation function, determining a kernel density estimation from the kernel estimation function based at least in part on the one or more parameters, and selecting an input value based at least in part on the kernel density estimation. The method may further include creating a corresponding synthetic target value based at least in part on the input value, augmenting the data set with the corresponding synthetic target value and input value to form a synthetic data set, and training a petrophysical interpretation machine learning model from the data set and the synthetic data set.

A downhole rock debris extraction system is provided. The downhole rock debris extraction system includes a downhole marking component, a ground control component and a ground detection and extraction component. The downhole marking component is arranged at a drill bit for underground excavation or near the drill bit, and is configured to mark rock debris by spraying different types of markers in real time during the underground excavation performed by the drill bit; the ground detection and extraction component is configured to detect the types of the markers on the rock debris; and the ground control component is configured to manage and control the downhole marking component to mark the rock debris, and to manage and control the ground detection and extraction component to determine the types of the markers on the rock debris. Furthermore, a control method for the downhole rock debris extraction system is provided.

A downhole rock debris extraction system is provided. The downhole rock debris extraction system includes a downhole marking component, a ground control component and a ground detection and extraction component. The downhole marking component is arranged at a drill bit for underground excavation or near the drill bit, and is configured to mark rock debris by spraying different types of markers in real time during the underground excavation performed by the drill bit; the ground detection and extraction component is configured to detect the types of the markers on the rock debris; and the ground control component is configured to manage and control the downhole marking component to mark the rock debris, and to manage and control the ground detection and extraction component to determine the types of the markers on the rock debris. Furthermore, a control method for the downhole rock debris extraction system is provided.

Overburden stress is applied to a core rock sample in a sleeve. Pressure is applied to pores in the core rock sample. An overburden fluid pressure indicative of the overburden stress and pore fluid pressure indicative of the pore pressure is measured. A difference between the overburden fluid pressure and pore fluid pressure is determined. The measuring and determination of the difference is repeated over a period of time. A rate of change of the difference over the period of time is determined. An indication of the rate of change meeting a threshold level is output indicative of the overburden stress transferring into and throughout the core rock sample.

Overburden stress is applied to a core rock sample in a sleeve. Pressure is applied to pores in the core rock sample. An overburden fluid pressure indicative of the overburden stress and pore fluid pressure indicative of the pore pressure is measured. A difference between the overburden fluid pressure and pore fluid pressure is determined. The measuring and determination of the difference is repeated over a period of time. A rate of change of the difference over the period of time is determined. An indication of the rate of change meeting a threshold level is output indicative of the overburden stress transferring into and throughout the core rock sample.

A method for determining a stratigraphic anomaly in a subsurface of the earth includes receiving raw data x, assigning the rock samples to corresponding stratigraphic units of the subsurface, transforming the raw data x to a centred log-ratios clr(x) dataset, calculating p-values with a pairwise sum rank test between populations of the centred log-ratios clr(x) dataset, selecting a set of fingerprint elements from the elements of the rock samples, converting raw concentrations corresponding to the set of fingerprint elements to isometric log-ratios ilr data, determining a number of ilr sub-populations within each stratigraphic unit, applying mixture discriminant analysis to the isometric log-ratios ilr data, using the ilr sub-populations to calculate posterior probabilities of the rock samples, and identifying the stratigraphic anomaly based on the posterior probabilities.