Systems, methods, and apparatuses for determining pore volume and pore volume compressibility of secondary porosity in rock samples is disclosed. In some implementations, determining a pore volume of a secondary porosity in a rock core sample may include saturating the rock sample with deuterium oxide (D2O) by applying a vacuum to the core sample covered by D2O; centrifuging the saturated rock sample at a selected rotational speed in the presence of a second fluid to displace a portion of the D2O from the rock sample with the second fluid; measuring the rock sample with low-field .sup.1H nuclear magnetic resonance (NMR) to determine a volume of the second fluid within the rock sample; and determining a pore volume associated with a secondary porosity based on the volume of the second fluid within the rock sample.

A method for determining a favorable time window of an infill well of an unconventional oil and gas reservoir, which comprises the following steps: S1, establishing a three-dimensional geological model with physical properties and geomechanical parameters; S2, establishing a natural fracture network model in combination with indoor core-logging-seismic monitoring; S3, calculating complex fractures in hydraulic fracturing of parent wells; S4, establishing an unconventional oil and gas reservoir model and calculating a current pore pressure field; S5, establishing a dynamic geomechanical model and calculating a dynamic geostress field; S6, calculating complex fractures in horizontal fractures of the infill well in different production times of the parent wells based on pre-stage complex fractures and the current geostress field; S7, analyzing a microseismic event barrier region and its dynamic changes in infill well fracturing; and S8, analyzing the productivity in different infill times, and determining an infill time window.

Permeability values are estimated based on well logs using regression algorithms, such as gradient boosting and random forest. The training data is selected from well logs for which core-analysis-based permeability values are available. The estimated permeability values are used to plan hydrocarbon production. The well logs used to build the depth blended model may include total porosity, gamma ray, volume of calcite, density, resistivity, and neutron logs. Selecting the training data may include grouping the well logs according to regions expected to have similar characteristics, choosing a subset of the well logs corresponding to wells expected to provide stable models according to pre-determined criteria, and/or identifying training zones on the chosen well logs according to one or more rules. Validation and consistency checks may also be performed.

Systems, methods, and apparatuses for determining pore volume and pore volume compressibility of secondary porosity in rock samples is disclosed. In some implementations, determining a pore volume of a secondary porosity in a rock core sample may include saturating the rock sample with deuterium oxide (D2O) by applying a vacuum to the core sample covered by D2O; centrifuging the saturated rock sample at a selected rotational speed in the presence of a second fluid to displace a portion of the D2O from the rock sample with the second fluid; measuring the rock sample with low-field .sup.1H nuclear magnetic resonance (NMR) to determine a volume of the second fluid within the rock sample; and determining a pore volume associated with a secondary porosity based on the volume of the second fluid within the rock sample.

Provided herein are systems and methods for modeling and simulating reservoir, wellbore, and hydraulic fracturing. The systems and methods provided herein may facilitate well life cycle simulation by integrating a three-dimensional model representative of hydraulic fracturing and fluid flow in a wellbore and reservoir. The systems and methods may couple fluid flow in the wellbore and reservoir during injection and extraction with propagation of fractures through subsurface materials during fluid injection. Integrated three-dimensional reservoir, wellbore, and hydraulic fracture simulation may be useful for the design of hydraulic fracture treatments and prediction of future reservoir production.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for determining a mudweight of drilling fluids in a hydrocarbon reservoir. One computer-implemented method includes: receiving pore pressure data of a rock formation in the hydrocarbon reservoir; determining permeability data of fractures of the hydrocarbon reservoir; determining Hoek-Brown failure criterion data; and determining a safe mudweight window based on the pore pressure data of the rock formation, the permeability data of the fractures, and the Hoek-Brown failure criterion data.

Systems, methods, and computer programs for monitoring production of fluids from a subterranean formation includes receiving, from a first sensor array at a first time, a first set of electromagnetic signals generated by an electroseismic or seismoelectric conversion of seismic signals caused, at least in part, by the production of fluid from the subterranean formation; receiving, from the first sensor array at a second time, a second set of electromagnetic signals generated by an electroseismic or seismoelectric conversion of seismic signals caused, at least in part, by the production of fluid from the subterranean formation; and determining one or more reservoir properties based, at least in part, on the first and second sets signals received from the first sensor array. The first sensor array are arranged to monitor the production operation.

A method is described for deriving high-resolution reservoir properties for a subsurface reservoir. The method may include receiving a seismic dataset; inverting the seismic dataset to generate an ensemble of coarse-scale seismic parameters, wherein the inverting may use one of Bayesian models with Markov Chain Monte Carlo (MCMC) sampling, simulated annealing, partial swarm, or analytic Bayes formulations; receiving fine-scale lithotype models; developing deep learning neural networks based on transfer learning using the fine-scale lithotype models to generate a conditional probability distribution of high-resolution reservoir parameters; generating an ensemble of high-resolution reservoir parameters using the deep learning neural network to condition the ensemble of coarse-scale seismic parameters; and displaying, on a user interface, the ensemble of high-resolution reservoir parameters. The method is executed by a computer system.

An exemplary embodiment of the invention relates a method for determining hydraulic permeability of rocks in a subsurface region, the method comprising: in-situ measuring and/or calculating at least one of independent seismic velocities of rocks at different locations in said subsurface region; determining at least one lithological unit in said subsurface region based on the measured seismic velocities; for the at least one lithological unit, acquiring at least one rock sample at an at least one location of said subsurface region; measuring the rock-sample porosity and permeability as functions of stress; measuring and/or calculating of at least one of independent seismic velocities of said at least one rock sample as functions of stress; computing the stiff and compliant porosity of said at least one rock sample; computing numerical coefficients of a given analytical permeability model based on the stiff and compliant porosities; computing coefficients of a given analytical model of an elastic-compliance characteristic of the rock based on the stiff and compliant porosity; computing the stiff and compliant porosity for a plurality of other locations in said subsurface region; and computing the permeability of rocks belonging to said at least one lithological unit, for said at least one location in said subsurface region and for said plurality of other locations in said subsurface region.

A method of fluid substitution, wherein an initial data set is provided, wherein a substituted data set is provided, wherein a rock physics model is provided, wherein the initial data set includes initial data of a geophysical parameter and initial fluid data, and wherein the substituted data set includes substituted fluid data. The method includes using the model and the initial data set to calculate first calculated data of the geophysical parameter, using the model and the substituted data set to calculate second calculated data of the geophysical parameter, calculating the difference between the first calculated data of the geophysical parameter and the second calculated data of the geophysical parameter, and applying the difference to the initial data of the geophysical parameter to produce substituted data of the geophysical parameter.