Abstract: A gas capture system is configured to purify gas streams. The gas capture system includes a first capture system including a plurality of first chambers interconnected by a first path. Each first chamber includes a first adsorbent. The gas capture system further includes a second capture system including a plurality of second chambers interconnected by a second path. Each second chamber includes a second adsorbent. The gas capture system further includes a third path connecting each first chamber to the second path such that a first output of the first capture system is input into the second capture system. The gas capture system further includes a fourth path connecting each second chamber to the first path such that a second output of the second capture system is input into the first capture system.

Abstract: Systems and methods for predicting fluid flow of porous media are provided. In implementations, a method includes: accessing, by a computing device, a capillary network representation of a porous medium sample; generating, by the computing device, a set of simplified network representations from the capillary network representation; determining, by the computing device, simulated fluid flow properties of each of the simplified network representations using a simulator to perform fluid flow simulations; and training, by the computing device, a neural network (NN) model utilizing the set of simplified network representations as inputs and the simulated fluid flow properties as model targets, thereby generating a trained NN model for predicting fluid flow properties of the porous medium.

Abstract: Provided are embodiments for a computer-implemented method, system, and device for tracking multiphase flow in a microfluidic device. Embodiments include receiving first readings from a first sensor of the microfluidic device, the first reading representing a detection of a fluid at an interface between the fluid and the first sensor, and receiving second readings from a second sensor of the microfluidic device, the second readings representing a detection of the fluid at an interface between the fluid and the second sensor, wherein the first sensor is located at a distance from the second sensor. Embodiments also include calculating a flow speed of the fluid in the microfluidic device based at least in part on a difference of time between the detections by the first sensor and the second sensor, and the distance between the first sensor and the second sensor.

Abstract: A method and system of discovering materials for use in carbon dioxide separation includes extracting references to chemical molecules from online sources. The extracted references are encoded into chemical formulas. Molecular properties are calculated from the encoded chemical formulas. Features are extracted from the chemical formulas. Molecular properties of predicted molecular structures are predicted through a machine learning engine. The predicted molecular properties are based on the calculated molecular properties and extracted features. Target properties for predicted molecular structures are defined. Synthesized molecular structures are generated. The synthesized molecular structures include predicted molecular properties satisfying the defined target properties.

Abstract: A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

Abstract: A computer implemented method generating query results is provided. The method includes generating by a computer processor, a training model through artificial intelligence. The training model may be based on annotated data. A knowledge base for a subject matter may be generated based on the training model. The knowledge base may be based on content from document sources related to the subject matter. A natural language query input from a user may be received. An intent and requirements for satisfying the intent may be inferred by the computer processor. The knowledge base may be referenced to extract information related to the intent and requirements, from documents in the knowledge base. Relationships between the extracted information and the requirements may be correlated from the documents in the knowledge base. In addition, query results may be displayed to the user. The query results are based on the correlated relationships.

Abstract: Evaluating enhanced oil recovery methods by identifying a first nanofluidic device associated with a reservoir rock formation and saturated with a target fluid, directing the injection of a secondary recovery fluid into the first nanofluidic device, determining a target fluid secondary recovery saturation level associated with injection of the secondary recovery fluid into the first nanofluidic device, identifying a second nanofluidic device associated with the reservoir rock formation and saturated with the target fluid, directing the injection of a tertiary recovery fluid into the second nanofluidic device, determining a target fluid tertiary recovery saturation level associated with the injection of the tertiary recovery fluid into the second nanofluidic device, determining a target fluid production efficiency associated with the tertiary recovery fluid, and providing the target fluid production efficiency to a user.

Abstract: Evaluating enhanced oil recovery methods by identifying a first nanofluidic device associated with a reservoir rock formation and saturated with a target fluid, directing the injection of a secondary recovery fluid into the first nanofluidic device, determining a target fluid secondary recovery saturation level associated with injection of the secondary recovery fluid into the first nanofluidic device, identifying a second nanofluidic device associated with the reservoir rock formation and saturated with the target fluid, directing the injection of a tertiary recovery fluid into the second nanofluidic device, determining a target fluid tertiary recovery saturation level associated with the injection of the tertiary recovery fluid into the second nanofluidic device, determining a target fluid production efficiency associated with the tertiary recovery fluid, and providing the target fluid production efficiency to a user.

Abstract: Aspects of the invention include determining, by a first AFM tip, a first snap-off force of a solid surface immersed in a first fluid, determining, by a second AFM tip, a second snap-off force, determining, by a third AFM tip, a third snap-off force, determining, by the first AFM tip, a fourth snap-off force of a droplet of the first fluid immersed in the second fluid on the solid surface, determining, by the second AFM tip, a fifth snap-off force, determining, by the third AFM tip, a sixth snap-off force, determining a first capillary force for first AFM tip and first droplet based on first snap-off force and fourth snap-off force, determining a second capillary force for second AFM tip and first droplet and a third capillary force for third AFM tip and first droplet, and determining interfacial tension between first fluid and second fluid based on the capillary forces.

Abstract: A system for manipulating electric fields within a microscopic fluid channel includes a fluid channel with an inlet and an outlet to support fluid flow, at least one controllable electric field producer that applies a non-uniform and adjustable electric field to one or more regions of the fluid channel, one or more sensors that measure one or more parameters of a fluid flowing through the fluid channel, and a controller with hardware and software components that receives signals from the one or more sensors representative of values of the one or more parameters and, based on the parameter values, drives one or more actuators to adjust the electric field produced by the plurality of electric field producers. A complex fluid including at least two components flows through the fluid channel, where at least one of the at least two components comprises particles controllable by the non-uniform and adjustable electric field.

Abstract: Aspects of the invention include determining, by a first AFM tip, a first snap-off force of a solid surface immersed in a first fluid, determining, by a second AFM tip, a second snap-off force, determining, by a third AFM tip, a third snap-off force, determining, by the first AFM tip, a fourth snap-off force of a droplet of the first fluid immersed in the second fluid on the solid surface, determining, by the second AFM tip, a fifth snap-off force, determining, by the third AFM tip, a sixth snap-off force, determining a first capillary force for first AFM tip and first droplet based on first snap-off force and fourth snap-off force, determining a second capillary force for second AFM tip and first droplet and a third capillary force for third AFM tip and first droplet, and determining interfacial tension between first fluid and second fluid based on the capillary forces.

Abstract: Provided are embodiments for a computer-implemented method, system, and device for tracking multiphase flow in a microfluidic device. Embodiments include receiving first readings from a first sensor of the microfluidic device, the first reading representing a detection of a fluid at an interface between the fluid and the first sensor, and receiving second readings from a second sensor of the microfluidic device, the second readings representing a detection of the fluid at an interface between the fluid and the second sensor, wherein the first sensor is located at a distance from the second sensor. Embodiments also include calculating a flow speed of the fluid in the microfluidic device based at least in part on a difference of time between the detections by the first sensor and the second sensor, and the distance between the first sensor and the second sensor.

Abstract: Provided are embodiments for a system for determining wettability of fluid-fluid-solid systems. The system includes a confocal optical microscope and a storage medium, the storage medium being coupled to a processor. The processor is configured to perform a scan of a sample of a multi-phase system using the confocal optical microscope, wherein a phase defines a structural phase of matter, identify each phase of the sample, and measure a three-phase contact line for the sample, wherein the three-phase contact line is along an interface of first fluid and a second fluid and an interface of a second fluid and solid. The processor is configured to obtain one or more characteristics from the sample based at least in part on the three-phase contact line, and provide the one or more characteristics for the sample. Also provided are embodiments for a method for determining the wettability of fluid-fluid-solid systems.

Abstract: A microfluidic device, including a controllable shape-changing micropillar where a shape of the shape-changing micropillar is changed by a fluid.

Abstract: A method of designing a nanoparticle tailored to support hydrocarbon recovery in a subterranean formation, a method for using nanoparticles to extract hydrocarbon from a subterranean formation, and a nanoparticle structure. The method may include determining environmental conditions of a subterranean formation, defining nanoparticle parameters based on the environmental conditions, and forming a nanoparticle comprising the nanoparticle parameters. The method may include producing a colloidal suspension of nanoparticles by mixing nanoparticles with water and injecting the colloidal suspension of nanoparticles into a subterranean formation. A nanoparticle structure may include a hydrophilic material in a defined three-dimensional shape having a maximum diameter. The nanoparticle may penetrate through an oil-water interface with an optimized contact angle, minimize an interfacial area between oil and water, and create an oil in water emulsion.

Abstract: A machine learning process is performed using one or more sources of information for enhanced oil recovery (EOR) materials to be used for an EOR process on a defined oil reservoir. Performance of the machine learning process produces an output comprising an indication of one or more EOR materials and their corresponding concentrations to be used in the EOR process. The indication of the one or more EOR materials and their corresponding concentrations is output to be used in the EOR process. Methods, apparatus, and computer program products are disclosed.

Abstract: Provided are embodiments for a system for determining wettability of fluid-fluid-solid systems. The system includes a confocal optical microscope and a storage medium, the storage medium being coupled to a processor. The processor is configured to perform a scan of a sample of a multi-phase system using the confocal optical microscope, wherein a phase defines a structural phase of matter, identify each phase of the sample, and measure a three-phase contact line for the sample, wherein the three-phase contact line is along an interface of first fluid and a second fluid and an interface of a second fluid and solid. The processor is configured to obtain one or more characteristics from the sample based at least in part on the three-phase contact line, and provide the one or more characteristics for the sample. Also provided are embodiments for a method for determining the wettability of fluid-fluid-solid systems.

Abstract: A method includes: performing a machine learning process using information for enhanced oil recovery (EOR) materials to be used for an EOR process on a defined oil reservoir; determining EOR materials suitable for a condition of the oil reservoir; listing the EOR materials; and outputting an indication of the EOR materials. The materials comprise a first complex fluid to be introduced into the oil reservoir. Determining the EOR materials suitable for the condition is based on similarities between a first set of vector values for the first complex fluid, a second set of vector values for a second complex fluid already in the oil reservoir, and geological data, each of the vector values of the first set being defined by parameters of the first complex fluid and each of the vector values of the second set being defined by parameters of the second complex fluid and the geological data.

Abstract: A method is provided including calculating a first property vector indicative of physical properties derived from a digital image of a first rock sample; determining a set of one or more similar rock samples by calculating a value indicating a similarity between the first rock sample and second rock samples based on the first property vector and second property vectors associated with the second rock samples; selecting a list of fluid additives based on existing enhanced fluid recovery efficiency values associated with the similar rock samples; performing, for each of the fluid additives, a simulation of a flow of fluid through a digital model of the first rock to determine a simulated enhanced fluid recovery efficiency value for the respective fluid additives; and outputting an optimal fluid additive for the first rock sample based at least in part on the calculated similarity values and simulated enhanced fluid recovery efficiency values.

Abstract: A method includes: performing a machine learning process using information for enhanced oil recovery (EOR) materials to be used for an EOR process on a defined oil reservoir; determining EOR materials suitable for a condition of the oil reservoir; listing the EOR materials; and outputting an indication of the EOR materials. The materials comprise a first complex fluid to be introduced into the oil reservoir. Determining the EOR materials suitable for the condition is based on similarities between a first set of vector values for the first complex fluid, a second set of vector values for a second complex fluid already in the oil reservoir, and geological data, each of the vector values of the first set being defined by parameters of the first complex fluid and each of the vector values of the second set being defined by parameters of the second complex fluid and the geological data.