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.

Methods and systems for quantifying an uncertainty in at least one porosity compaction model parameter are disclosed. The method includes obtaining a first sequence of depth-porosity duplets from a sedimentary layer and generating a plurality of alternate sequences of depth-porosity duplets based, at least in part, on resampling the first sequence. The method further includes estimating a plurality of values for the porosity compaction model parameter based on fitting a porosity compaction model to the first sequence and each alternate sequence. The method further includes quantifying the uncertainty in the porosity compaction model parameter based on determining the value of a parameter of probability density function fit to a histogram of the plurality of values for the porosity compaction model parameter.

Methods and systems for quantifying an uncertainty in at least one porosity compaction model parameter are disclosed. The method includes obtaining a first sequence of depth-porosity duplets from a sedimentary layer and generating a plurality of alternate sequences of depth-porosity duplets based, at least in part, on resampling the first sequence. The method further includes estimating a plurality of values for the porosity compaction model parameter based on fitting a porosity compaction model to the first sequence and each alternate sequence. The method further includes quantifying the uncertainty in the porosity compaction model parameter based on determining the value of a parameter of probability density function fit to a histogram of the plurality of values for the porosity compaction model parameter.

Methods are provided for characterizing organic matter in a geological rock formation that perform a Raman spectroscopy measurement on a rock sample of the formation to acquire a Raman spectrum of the rock sample. At least one processor is used to perform operations that involve i) analyzing the Raman spectrum of the rock sample to determine data characterizing at least one Raman spectral feature corresponding to the rock sample, ii) 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 given the data characterizing the at least one Raman spectral feature as input, 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.

Provided is a sample collecting method for collecting a sample of soil at a predetermined depth by excavation on an extraterrestrial body or the earth. The method comprises forming a first borehole 70 that reaches a first depth from a land surface 69 by preliminary excavation of soil, the first depth being less than a predetermine sampling depth from the land surface 69; and forming a second borehole 73 that has a smaller opening than the first borehole and reaches a second depth from the land surface by further excavation of soil in the first borehole, the second depth being equal to or greater than the sampling depth, wherein, while forming the second borehole 73, part of soil present at the sampling depth in the second borehole 73 is transferred to the land surface 69 as a sample for analysis.

Provided is a sample collecting method for collecting a sample of soil at a predetermined depth by excavation on an extraterrestrial body or the earth. The method comprises forming a first borehole 70 that reaches a first depth from a land surface 69 by preliminary excavation of soil, the first depth being less than a predetermine sampling depth from the land surface 69; and forming a second borehole 73 that has a smaller opening than the first borehole and reaches a second depth from the land surface by further excavation of soil in the first borehole, the second depth being equal to or greater than the sampling depth, wherein, while forming the second borehole 73, part of soil present at the sampling depth in the second borehole 73 is transferred to the land surface 69 as a sample for analysis.

This disclosure relates to a method and a system for sequestering carbon-containing materials in underground wells. An example method includes: obtaining a material comprising a carbon-containing liquid; optionally testing the material for compatibility with an underground well; optionally adjusting a property of the material to improve the compatibility; and providing the material for injection into the underground well.

This disclosure relates to a method and a system for sequestering carbon-containing materials in underground wells. An example method includes: obtaining a material comprising a carbon-containing liquid; optionally testing the material for compatibility with an underground well; optionally adjusting a property of the material to improve the compatibility; and providing the material for injection into the underground well.

The present disclosure provides a method and system for predicting a relative permeability curve based on machine learning. The present disclosure takes logging curve data as an input, and water saturation endpoint values as an output to establish a first relative permeability curve starting point model, and takes the logging curve data and a predicted water saturation starting value output from the first relative permeability curve starting point model as an input, and relative permeabilities under different water saturations as an output to establish a first relative permeability model, thereby obtaining a comprehensive prediction method for the relative permeability curve based on deep learning, and implying control mechanisms and parameters to a model.

A modular system for analyzing drilled cuttings includes a sampler unit, a washer unit, an analysis unit, and a central processing unit. The sampler unit receives the drilled cuttings from a shale shaker disposed on a rig site that obtains the drilled cuttings. The washer unit removes debris from the drilled cuttings. The analysis unit determines lithological properties of the drilled cuttings. The packager unit packages the drilled cuttings. The central processing unit coordinates operations to process the drilled cuttings through each of the sampler unit, washer unit, analysis unit and packager unit. The central processing unit facilitates a processing link among the sampler unit, washer unit, analysis unit and packager unit so that the sampler unit, washer unit, analysis unit and packager unit are integrated to form the modular system.