It discloses a hypergravity experimental apparatus and experimental method for interaction between brittle deformation and ductile deformation. The experimental apparatus comprises an experiment module, a control device and a drive device; the drive device comprises a centrifuge for generating a hypergravity environment and a hydraulic press for generating extensional/compressional force in an experiment box; the control device comprises a control terminal, a control cabinet and a hydraulic control station for controlling the operation of the drive device; the experiment module is provided with an experiment box and a transmission device therein, and the transmission device converts a vertical lifting force generated by a hydraulic cylinder controlled by the hydraulic press in the drive device into a horizontal pushing-pulling force.

Methods and systems for reservoir characterization use identification of lithostratigraphic layers within a subterranean formation based on rock geochemistry of the subterranean formation. This approach includes: collecting rock samples related to lithostratigraphy of target wells in the subterranean formation; measuring geochemical/mineralogical parameters of the rock samples with laboratory equipment; measuring geochemical/mineralogical parameters of the subsurface formation using wellbore geochemical logging tools in the target wells; measuring formation acoustic velocities for the target wells; generating characteristic rock sample and log signature patterns for different lithostratigraphic layers based on the measured geochemical/mineralogical parameters and acoustic velocities associated with the different lithostratigraphic layers identified in the target wells; combining the characteristic log signatures for the different lithostratigraphic layers into a lithographic interpretation using neutron capture spectroscopy model; and identifying the lithostratigraphic layers within the subterranean formation by applying the model to well logs of non-target wells.

Methods for predicting hydrocarbon production rates for a hydrocarbon reservoir include receiving data from a hydrocarbon reservoir. The data includes reservoir characterization data, well log data, and hydraulic fracturing data. A physics-constrained machine learning model predicts a hydrocarbon production rate for the hydrocarbon reservoir as a function of time. The physics-constrained machine learning model includes an artificial neural network and a hydrocarbon fluid flow model. Predicting the hydrocarbon production rate includes generating, by the artificial neural network, multiple parameters of the hydrocarbon fluid flow model based on the data from the hydrocarbon reservoir. The hydrocarbon fluid flow model provides the predicted hydrocarbon production rate as a function of time based on the parameters. A display device of the computer system presents the predicted hydrocarbon production rate for the hydrocarbon reservoir as a function of time.

An approach for reservoir characterization is based on rock geochemistry of the subterranean formation. This approach includes: collecting rock samples related to lithostratigraphy of target wells; measuring geochemical/ mineralogical parameters of the rock samples; measuring geochemical/mineralogical parameters of the subsurface formation; measuring formation acoustic velocities for the target wells; generating characteristic rock sample and log signature patterns for different lithostratigraphic layers based on the measured geochemical/mineralogical parameters and acoustic velocities associated with the different lithostratigraphic layers identified in the target wells; combining the characteristic log signatures for the different lithostratigraphic layers into a lithographic interpretation using neutron capture spectroscopy model; and identifying the lithostratigraphic layers within the subterranean formation by applying the model to well logs of non-target wells.

Systems and methods of surface steering control of drilling may be used together with systems and methods for planning one or more wells before drilling, planning a well path during drilling and/or updating that well plan and/or other well plans during the drilling of a well. The methods and systems may include planning a field, comprising a plurality of wells to be drilled and/or a plurality of pads from which a plurality of wells are to be drilled, planning a pad from which a plurality of wells are to be drilled, and planning a well both before and during drilling of the well.

A method of waterflood management for reservoir(s) having production hydrocarbon-containing well(s) including injector well(s). A reservoir model has model parameters in a mathematical relationship relating a water injection rate to a total production rate of the production well including at least one of a hydrocarbon production rate and water production rate. A solver implements automatic differentiation utilizing training data regarding the reservoir including operational data that includes recent sensor and/or historical data for the water injection rate and the hydrocarbon production rate, and constraints for the model parameters. The solver solves the reservoir model to identify values or value distributions for the model parameters to provide a trained reservoir model. The trained reservoir model uses water injection schedule(s) for the injector well to generate predictions for the total production rate.

Systems and methods are provided for determining and presenting field view history-matched well quality data. In one embodiment, a method includes receiving well data for a plurality of wells and performing a plurality of functional operations including a trend operation to determine well groups using pattern recognition of well time lapse pressure trends, the trend operation configured to identify at least one connected reservoir region (CRR), a geo-probe integration operation configured to integrate data for each CRR and evaluate a three-dimensional (3D) static model for wells; a history match advisor operation to generate a combined display of time dependent and depth dependent representation of the well data; a spatio-temporal operation configured to generate a space and time visualization of the well data; a front operation configured to track simulated injected fluid front; and an insight operation configured to report static changes between a well field model and history match model.

A method can include receiving spatially located geophysical data of a geologic region as acquired by one or more sensors; solving a system of equations for multi-dimensional implicit function values within a multi-dimensional space that represents the geologic region where the system of equations are subject to a smoothness constraint and subject to a weighted curvature minimization criterion at a plurality of spatially located points based on the spatially located geophysical data; and rendering to a display, a structural model of the geologic region based at least in part on the multi-dimensional implicit function values where the structural model characterizes stratigraphy of the geologic region.

2D slices/images may be extracted from a three-dimensional volume of subsurface data. Image comparison analysis across sequential 2D slices/images may identify boundaries within the corresponding subsurface region, such as changes in style of deposition or reservoir property distribution. Identification of temporal/spatial boundaries in the subsurface region where subsurface properties change may facilitate greater understanding of the scales and controls on heterogeneity, and connectivity between different locations.

Provided are techniques for developing a hydrocarbon reservoir that include: determining a reservoir model of a hydrocarbon reservoir that includes columns of gridblocks that represent a vertical segment of the reservoir; acquiring nano-images of a rock sample of the reservoir; determining, based on the nano-images, properties of an inorganic pore network and an organic pore network of the rock sample; generating a divided reservoir model of the reservoir that represents the inorganic and organic pore networks of the reservoir, including: for each of the columns of gridblocks, dividing each of the gridblocks of the column into: a water-wet gridblock associated with the properties of the inorganic pore network determined based on the nano-images; and an oil-wet gridblock associated with the properties of the organic pore network determined based on the nano-images; and generating, using the divided reservoir model, a simulation of the hydrocarbon reservoir.