Landmark Graphics Corporation

Emc IP Holding Company LLC

Halliburton Energy Services, Inc.

Crowley Government Services, Inc.

Ashwani Dev

Keshava Prasad Rangarajan

Satyam Priyadarshy

Vinicius Michel Gottin

Jeffrey Marc Yarus

Jonas Furtado Dias

Scott David Senften

Joseph Blake Winston

Romulo D Pinho

Youli Mao

Xi Wang

David Michael Holmes

Chandra Yeleshwarapu

Angelo Ciarlini

Edward Pacheco

Bhaskar Mandapaka

Andre Maximo

Clifton Brent Walker

Srinath Madasu

Suresh Venugopal

Jin Fei

Ajay Pratap Singh

Trace Boone Smith

Sridharan Vallabhaneni

Nagaraj Srinivasan

Geetha Gopakumar Nair

Raquel Morag Velasco

Roxana Nielsen

Raja Vikram Pandya

Nagaraj Sirinivasan

Shiju Zacharia

Smijith Kunhiraman

Patrick Wallace

David Holmes

Vinicius Gottin

Jonas F Dias

A collapsible container that includes a base, a first side panel hingedly attached to the base, a second side panel hingedly attached to the base, a first end panel hingedly attached to the base, with the first end panel including a door, and a second end panel hingedly attached to the base. The collapsible container can further include a top cover. The collapsible container is configured to be positioned in a collapsed position and an assembled position. In an assembled position, the collapsible container is configured to hold items during a domestic or international move.

Collapsible shipping container

A method may comprise: modeling a complex fracture network within the subterranean formation with a mathematical model based on a natural fracture network map and measured data of the subterranean formation collected in association with a fracturing treatment of the subterranean formation to produce a complex fracture network map; importing microseismic data collected in association with the fracturing treatment of the subterranean formation into the mathematical model; identifying directions of continuity in the microseismic data via a geostatistical analysis that is part of the mathematical model; and correlating the directions of continuity in the microseismic data to the complex fracture network with the mathematical model to produce a microseismic-weighted (MSW) complex fracture network map.

Geostatistical analysis of microseismic data in fracture modeling

A computer implemented method, computer program product, and system for managing execution of a workflow comprising a set of subworkflows, comprising optimizing the set of subworkflows using a deep neural network, wherein each subworkflow of the set of subworkflows has a set of tasks, wherein each task of the sets of tasks has a requirement of resources of a set of resources; wherein each task of the sets of tasks is enabled to be dependent on another task of the sets of tasks, training the deep neural network by: executing the set of subworkflows, collecting provenance data from the execution, and collecting monitoring data that represents the state of said set of resources, wherein the training causes the neural network to learn relationships between the states of said set of resources, the said sets of tasks, their parameters and the obtained performance, optimizing an allocation of resources of the set of resources to each task of the sets of tasks to ensure compliance with a user-defined quality metric based on the deep neural network output.

Workflow optimization

The subject disclosure provides for a mechanism implemented with neural networks through machine learning to predict wear and relative performance metrics for performing repairs on drill bits in a next repair cycle, which can improve decision making by drill bit repair model engines, drill bit design, and help reduce the cost of drill bit repairs. The machine learning mechanism includes obtaining drill bit data from different data sources and integrating the drill bit data from each of the data sources into an integrated dataset. The integrated dataset is pre-processed to filter out outliers. The filtered dataset is applied to a neural network to build a machine learning based model and extract features that indicate significant parameters affecting wear. A repair type prediction is determined with the applied machine learning based model and is provided as a signal for facilitating a drill bit operation on a cutter of the drill bit.

Drill bit repair type prediction using machine learning

A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

Fault detection based on seismic data interpretation

A method includes receiving a training selection of a first set of faults located in a first subset of a seismic dataset for a subsurface geologic formation, detecting a second set of faults in the seismic dataset based on fault interpretation operations using a first set of interpretation parameters, and determining a difference between the first set of faults and the second set of faults. The method also includes generating a second set of interpretation parameters for the fault interpretation operations based on the difference between the first set of faults and the second set of faults, and determining a feature of the subsurface geologic formation based on fault interpretation operations using the second set of interpretation parameters.

Hybrid optimization of fault detection and interpretation

A multivariate analysis may be used to correlate seismic attributes for a subterranean formation with petrophysical properties of the subterranean formation and/or microseismic data associated with treating, creating, and/or extending a fracture network of the subterranean formation. For example, a method may involve modeling petrophysical properties of a subterranean formation, microseismic data associated with treating a complex fracture network in the subterranean formation, or a combination thereof with a mathematical model based on measured data, microseismic data, completion and treatment data, or a combination thereof to produce a petrophysical property map, a microseismic data map, or a combination thereof; and correlating a seismic attribute map with the petrophysical property map, the microseismic data map, or the combination thereof using the mathematical model to produce at least one quantified correlation, wherein the seismic attribute map is a seismic attributed modeled for the complex fracture network.

Multivariate analysis of seismic data, microseismic data, and petrophysical properties in fracture modeling

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Katy, TX, United States of America

Ashwani Dev