Abstract: A method includes modeling a reservoir using a lab scale set of models and a field scale set of models. The reservoir is modeled using the lab scale set of models by scanning a sample of the reservoir into the lab scale set of models to create modeled fractures, estimating hydraulic properties of the modeled fractures, estimating multi-phase dynamic properties of the modeled fractures, and determining characteristics of a flow regime of a fluid flowing through the modeled fractures. The reservoir is modeled using the field scale set of models by modeling a discrete fracture network of the reservoir, upscaling the discrete fracture network, and calibrating the discrete fracture network. An enhanced oil recovery operation is designed and performed on the reservoir using the calibrated discrete fracture network.

Abstract: A safety-enhanced center pillar structure for a vehicle includes: a reinforced outer portion provided on the inner side of a center pillar of the vehicle; a reinforced inner portion provided on the inner side of the vehicle in a manner that is spaced apart from the reinforced outer portion; and a connection member. The connection member is positioned between the reinforced outer portion and the reinforced inner portion and connects the reinforced outer and reinforced inner portions to each other. One portion of the connection member is positioned over an upper horizontal surface of a side sill outer portion. Thus, the connection member creates a predetermined distance together with the upper horizontal surface of the side sill outer portion.

Abstract: Methods and systems are disclosed. Methods may include obtaining well data along an interval of a well and identifying a first candidate interval within the interval using the well data. The well data includes acoustic data and porosity data. A formation surrounding the well within the first candidate interval includes micropores. The methods may further include determining mechanical parameter data along the first candidate interval based on the acoustic data and identifying a second candidate interval within the first candidate interval based on the mechanical parameter data and the porosity data. The methods may still further include determining water saturation data along the second candidate interval based on the well data, identifying a productive interval within the second candidate interval based on the water saturation data and a water saturation threshold, and determining a completion plan for the well based on the productive interval.

Abstract: A method may include obtaining fracture image data regarding a geological region of interest. The method may further include determining various fractures in the fracture image data using a first artificial neural network and a pixel-searching process. The method may further include determining a fracture model using the fractures, a second artificial neural network, and borehole image data. The method may further include determining various fracture permeability values using the fracture model and a third artificial neural network. The method may further include determining various matrix permeability values for the geological region of interest using core sample data. The method may further include generating a coarsened grid model for the geological region of interest using a fourth artificial neural network, the matrix permeability values, and the fracture permeability values.

Abstract: Systems and methods for correcting NMR T2 times are disclosed. The method may include creating a plurality of 3D rough surface pore models, where each 3D rough surface pore model includes a rough surface, for each of the plurality determining a pore roughness coefficient (PRC) and a volume; and simulating a first T2 relaxation time based on the 3D rough surface pore model. The method may further include determining a smooth surface pore model, with the same volume as the 3D rough surface pore model, and simulating a second T2 relaxation time based on the smooth surface pore model. The method may still further includes determining a T2 rough surface correction factor based on the first and the second T2 relaxation times, and forming a data pair comprising the pore roughness coefficient (PRC) and the T2 rough surface correction factor; and fitting a T2 correction curve to the data pairs.

Abstract: Techniques for determining reservoir formation properties include generating digital models of core samples taken from one or more reservoir formations based on corresponding images of the core samples; determining a pore throat size distribution of the core samples based on the generated digital models; determining corresponding capillary pressure curves and corresponding NMR value distributions of the core samples with one or more numerical simulations; generating one or more machine-learning (ML) models based on the pore throat size distribution, corresponding capillary pressure curves, and corresponding NMR value distributions of the core samples; adjusting the one or more ML models with one or more reservoir data of the one or more reservoir formations; generating adjusted capillary pressure curves and NMR value distributions from the one or more adjusted ML models; and determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or

Abstract: Methods and systems are disclosed. The methods may include collecting matrix training data along a first depth interval of a first well and training a first artificial intelligence (AI) model using the matrix training data. The methods may further include collecting well data along a depth interval of a well, inputting the well data into the first AI model, and producing a predicted matrix permeability log along the depth interval from the first AI model. The methods may still further include collecting an image at a discrete depth within the depth interval of the well, where the image is of a fracture, inputting the image into a second AI model, producing a predicted fracture permeability from the second AI model, and generating the predicted hybrid-permeability log using the predicted matrix permeability log and the predicted fracture permeability.

Abstract: A method and system for labeling and identifying a sample, including a rock core sample, after determining a physical primary label has been compromised is provided. The method includes labeling the sample by placing a physical primary label on the sample or sample container, creating a first rollout image of the sample for a secondary label, and storing the first rollout image in a secondary label digital database with a sample identifier. The method continues for identifying the sample after determining the physical primary label has been compromised by creating a secondary rollout image of the sample and searching the secondary label digital database using the second rollout image to create a plurality of matching scores using a machine learning or deep learning image matching technique. The matching scores are then evaluated to determine an identify of a sample and the sample is re-labelled using a replacement primary label.

Abstract: A learning method for generating a multiphase collateral image comprises the steps of: receiving inputs of an MRI image of a head part and a multiphase collateral image generated on the basis of the MRI image; generating a brain mask by using the MRI image; generating an MRI image and a multiphase collateral image which are masked by the brain mask; and learning the masked multiphase collateral image for the masked MRI image by using a learning network.

Abstract: Techniques for determining reservoir formation properties include generating digital models of core samples taken from one or more reservoir formations based on corresponding images of the core samples; determining a pore throat size distribution of the core samples based on the generated digital models; determining corresponding capillary pressure curves and corresponding NMR value distributions of the core samples with one or more numerical simulations; generating one or more machine-learning (ML) models based on the pore throat size distribution, corresponding capillary pressure curves, and corresponding NMR value distributions of the core samples; adjusting the one or more ML models with one or more reservoir data of the one or more reservoir formations; generating adjusted capillary pressure curves and NMR value distributions from the one or more adjusted ML models; and determining a reservoir formation specific pore throat size distribution from at least one of the adjusted capillary pressure curves or

Abstract: A method includes providing an injection well extending from a surface into a formation and alternatingly pumping a foam injectant and a gel injectant into the injection well, wherein the foam injectant includes a liquid phase with a surfactant and water and a gas phase and the gel injectant includes a water-soluble polymer and a crosslinker. The foam injectant and the gel injectant are injected at separate times and sequentially.

Abstract: A method may include obtaining first nuclear magnetic resonance (NMR) data for a saturated core sample regarding a geological region of interest. The method may further include obtaining second NMR data for a desaturated core sample regarding the geological region of interest. The method may further include determining a geological model for the geological region of interest using the first NMR data based on the saturated core sample and the second NMR data based on the desaturated core sample.

Abstract: A method for determining the pore size distribution in a reservoir, including the steps: drilling a core sample out of the reservoir, determining a porosity distribution along the core sample, obtaining T2-distributions at different saturation levels of the core sample with formation brine, performing time domain subtraction on the T2-distributions to obtain T2-distributions at all saturation levels, determining the pore throat size distribution along the core sample, determining first porosities from the T2-distributions that correspond to second porosities of the pore throat size distribution for each saturation level, determining T2-distributions at the first porosities from the T2-distributions, determining pore throat sizes at the second porosities from the pore throat size distributions, plotting the pore throat sizes as function of the relaxation times T2 to obtain the surface relaxation, and determining the pore size distribution of the reservoir.

Abstract: A method includes deriving spatial permeability along a core axis by saturating the rock with an aqueous solution, performing T2 NMR on the saturated rock to detect spatial NMR data along the core axis, desaturating the rock, performing T2 NMR on the desaturated rock to detect spatial NMR data along the core axis, determining the spatial cutoff data for the saturated and desaturated rock along the core axis, and analyzing the spatial NMR data. The method further includes deriving spatial permeability along a second core axis by additionally performing T2 NMR on the saturated rock to detect spatial NMR data along a second core axis, performing T2 NMR on the desaturated rock to detect spatial NMR data along a second core axis, and determining the spatial cutoff data for the saturated and desaturated rock along the second core axis.

Abstract: A method may include obtaining first nuclear magnetic resonance (NMR) data for a saturated core sample regarding a geological region of interest. The method may further include determining, using the first NMR data, spatial porosity data based on the saturated core sample. The spatial porosity data may describe various porosity values as a function of a sampling position of the saturated core sample. The method may further include obtaining second NMR data for a desaturated core sample regarding the geological region of interest. The method may further include determining, using the second NMR data, spatial permeability data based on the desaturated core sample. The method may further include determining a geological model for the geological region of interest using the spatial porosity data, the spatial permeability data, and a fitting process.

Abstract: A method is disclosed for modeling fluid flow through a fracture. The method includes obtaining a normal stress, and a shear stress and determining a first three-dimensional (3D) aperture model. Further, the method includes estimating a normal fracture closure displacement under the normal stress and a fracture dilation under the shear stress and simulating a fluid flow through a second 3D aperture model of the fracture. The second 3D aperture model of the fracture is based on the first 3D aperture, the normal fracture closure displacement, and the fracture dilation. Additionally, the method includes calculating a permeability of the second 3D aperture model of the fracture based on the simulated fluid flow.

Abstract: A method for determining the pore size distribution in a reservoir, including the steps: drilling a core sample out of the reservoir, determining a porosity distribution along the core sample, obtaining T2-distributions at different saturation levels of the core sample with formation brine, performing time domain subtraction on the T2-distributions to obtain T2-distributions at all saturation levels, determining the pore throat size distribution along the core sample, determining first porosities from the T2-distributions that correspond to second porosities of the pore throat size distribution for each saturation level, determining T2-distributions at the first porosities from the T2-distributions, determining pore throat sizes at the second porosities from the pore throat size distributions, plotting the pore throat sizes as function of the relaxation times T2 to obtain the surface relaxation, and determining the pore size distribution of the reservoir.

Abstract: A method includes providing an injection well extending from a surface into a formation and alternatingly pumping a foam injectant and a gel injectant into the injection well, wherein the foam injectant includes a liquid phase with a surfactant and water and a gas phase and the gel injectant includes a water-soluble polymer and a crosslinker. The foam injectant and the gel injectant are injected at separate times and sequentially.

Abstract: A method may include obtaining first nuclear magnetic resonance (NMR) data for a saturated core sample regarding a geological region of interest. The method may further include determining, using the first NMR data, spatial porosity data based on the saturated core sample. The spatial porosity data may describe various porosity values as a function of a sampling position of the saturated core sample. The method may further include obtaining second NMR data for a desaturated core sample regarding the geological region of interest. The method may further include determining, using the second NMR data, spatial permeability data based on the desaturated core sample. The method may further include determining a geological model for the geological region of interest using the spatial porosity data, the spatial permeability data, and a fitting process.

Abstract: A method for history matching utilizing Bayesian Markov Chain Monte Carlo (MCMC) workflow may include selecting a reservoir simulation model of interest, identifying a mathematical model relevant to the reservoir simulation model, and identifying a plurality of history matching parameters as initial priors. The method may include constructing a first model, utilizing the initial priors, to obtain updated priors. The method may include constructing a second model to obtain posteriors. The method may include determining history matching accuracy of the reservoir simulation model by comparing medians of the posteriors and a plurality of measured data. The method may further include, upon determining accuracy of the reservoir simulation model, performing a plurality of predictions of a reservoir.