Hewlett-packard Development Company, L.p.

Chevron U.s.a. Inc.

Landmark Graphics Corporation

Landmark Graphics Corporaton

Amit Kumar Singh

Robello Samuel

Kevin Williams

Xinghui Liu

Keshava Prasad Rangarajan

Binh T Truong

John Landry

Olivier Germain

Christopher Neil Marland

Gaurav Sujit Roy

Clyde Dean Wehunt

Jiehao Wang

Mengqi Hei

Serkan Dursun

Quoc P Pham

Augusto Queiroz De Macedo

Margaretha Catharina Maria Rijken

Matthew C Jones

Ronnie Glenn Blewer

Prateek Tiwari

Nileshkumar Gawali

Sakshi Kandya

Frederick Lee

Hermanus Teunissen

Sahil Malhotra

Ram N Adari

David Gosman

Suzanne Broussard

Aleksei Shelaev

Sarvesh J Naik

Madhurya Sarma

Aravind Iyengar

Siddhesh Arolkar

Chetan Satpute

Sumit Raut

Ram Naresh Adari

Olivier Roger Germain

Examples are described herein for monitoring power source usage of a computing device, including a battery and another power source, over time. In various examples, time periods of this monitored time may be added to a time count based on their immediately previous or immediately subsequent time periods satisfying a condition. The time count may be used to track periods of time during which certain power source usage factors are present that may lead to or may indicate the presence of battery swelling. After a time period is added to the time count, the updated time count can be compared to a time count threshold, and a user of the computing device may be warned based on the comparison. The threshold and warning may be selected to warn a user of potential present or future swelling of the battery.

Detect and prevent battery swelling

An example of an apparatus including a network interface to receive enrollment data from a device, wherein the device is identified by a device identifier. The apparatus further includes a memory storage unit connected to the network interface. The memory storage unit receives device history data associated with the device identifier while the device is enrolled. The memory storage unit is also to maintain a database to store the device history data associated with the device identifier. The apparatus also includes a processor to receive a de-enrollment command associated with the device via the network interface. The de-enrollment command causes the processor to modify the device identifier stored in the database.

Databases to store device history data

One embodiment of a wellbore to fracture connectivity apparatus comprises an opening portion representing an opening in a wellbore for receiving fluid, proppant, or any combination thereof to generate at least one fracture in a formation; a fracture portion representing the at least one fracture in the formation; and a coupling portion representing wellbore to fracture connectivity between the opening portion and the fracture portion. The coupling portion couples the opening portion and the fracture portion.

Wellbore to fracture connectivity

The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Assessing spatial movement behavior

An example of an apparatus including a network interface to receive first metadata and second metadata. The first metadata is associated with a first data source and the second metadata is associated with a second data source. The apparatus includes a processor to determine a first series of variance values associated with first metadata and second metadata, and to determine a second series of variance values associated with first metadata and second metadata. Furthermore, the apparatus includes a memory storage unit to store the first series of variance values and the second series of variance values. The apparatus includes an analysis engine to analyze the first series of variance values and the second series of variance values to confirm compatibility between the first data source and the second data source. The second series of variance values is to be analyzed after the first series of variance values is passed.

Metadata variance analytics

An example of an apparatus including a memory storage unit to store telemetry data collected from a plurality of sources, wherein each source of the plurality of sources maintains confidentiality. The apparatus further includes an anonymizing engine to remove identifying information from the telemetry data to generate anonymized data. The apparatus also includes a communication interface to receive request from a client device for an upgrade. The request includes a requesting device configuration of the client device. The apparatus also includes a selection engine to select a subset of the anonymized data based on the requesting device configuration. Furthermore, the apparatus includes a comparison engine to analyze the subset of the anonymized data to determine a probability of an upgrade failure at the client device. Additionally, the apparatus includes an upgrade engine to implement the upgrade on the client device based on the probability.

Upgrades based on analytics from multiple sources

Embodiments of determining proppant distribution for a plurality of clusters within a fracture stage of a wellbore are provided herein. The embodiments include performing computational fluid dynamics modeling on at least a portion of a wellbore without any openings and a portion of the wellbore comprising at least one opening along a single azimuth to determine proppant efficiency for the at least one opening along the single azimuth while simulating flow of fluid, proppant, or any combination thereof through the wellbore, an equilibrium proppant concentration profile for the portion of the wellbore without any openings, and an equilibrium velocity profile for the portion of the wellbore without any openings. The embodiment further comprises determining proppant efficiency for at least one other opening of the wellbore at a different azimuth, generating a model that correlates the single azimuth among other items, and using the model to determine the proppant distribution.

Determining proppant distribution for a plurality of clusters within a fracture stage

Embodiments of determining a hydraulic fracture completion configuration for a wellbore that extends through a subterranean formation are provided herein. Embodiments of performing a hydraulic fracturing operation on a wellbore that extends through a subterranean formation are also provided herein.

Determining a hydraulic fracture completion configuration for a wellbore

Example implementations relate to a retail point of sale (RPOS) device and cloud service. An example system can include an RPOS agent controller and a cloud service controller communicatively coupled to one another. The cloud service controller can provide a notification of a state of the RPOS device.

Managing retail point of sale devices

A system is provided including a memory in communication with a processor. The memory is to store a value of an attribute. The processor is to obtain from a source data structure the value. The processor is also to store in a further data structure the value in association with a descriptor identifying the source data structure. Moreover, the processor is to store in the further data structure an identifier in association with the value. The identifier is incrementable and unique to the value among values associated with the descriptor. In addition, the processor is to output the further data structure.

Data structure with identifiers

An example of an apparatus including a network interface to receive data about a data application via an external network is provided. The apparatus further includes a collection engine in communication with the network interface to receive the data. The apparatus also includes a metadata generation engine to generate application metadata associated with the data application. The apparatus includes a memory storage unit to maintain a database of metadata. The database includes the application metadata associated with the data application. The apparatus also includes a processor to apply the database of metadata to a lookup table.

Databases to store metadata

There is provided a method including determining whether a selection of database query identifiers is to be made in a fixed manner or a dynamic manner. The method also includes generating a collection including the selection from a list including database query identifiers related to a database. When the selection is to be made in the fixed manner, the selection of the database query identifiers includes a view name and/or database query logic. When the selection is to be made in the dynamic manner, the selection of the database query identifiers includes a corresponding view name. The method also includes generating a materialized view corresponding to a given database query identifier in the collection. Moreover, the method includes storing an identifier of the materialized view in association with the given database query identifier.

Materialized view generation

A system is provided including a memory in communication with a processor. The memory is to store a data source comprising source data. The processor is to store in a row of a table: a source identifier of the data source, a data category value, and an aggregation identifier of an aggregation operation. The processor is also to execute the aggregation operation against the source data based on the data category value to obtain a measure related to the source data. In addition, the processor is to store the measure in the row, and output the table.

Data aggregation data structure

Building models and predicting operational outcomes of a drilling operation. At least some of the illustrative embodiments are methods including: gathering sensor data regarding offset wells and context data regarding the offset wells, and placing the sensor data and context data into a data store; creating a reduced data set by identifying a correlation between data in the data store and an operational outcome in a drilling operation; creating a model based on the reduced data set; and predicting the operational outcome based on the model.

Growing community of inventors

Houston, TX, United States of America

Amit Kumar Singh