Siriwardane, 2024 - Google Patents
Overview of SMART InitiativeSiriwardane, 2024
View PDF- Document ID
- 14462543837763424422
- Author
- Siriwardane H
- Publication year
External Links
Snippet
The objective of the SMART Initiative, ie, Science-informed Machine Learning (ML) for Accelerating Real-Time Decisions in Subsurface Applications, is to show how the utilization of ML can significantly improve efficiency and effectiveness of field-scale commercial carbon …
- 238000010801 machine learning 0 abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/282—Application of seismic models, synthetic seismograms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V99/00—Subject matter not provided for in other groups of this subclass
- G01V99/005—Geomodels or geomodelling, not related to particular measurements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/003—Seismic data acquisition in general, e.g. survey design
- G01V1/005—Seismic data acquisition in general, e.g. survey design with exploration systems emitting special signals, e.g. frequency swept signals, pulse sequences or slip sweep arrangements
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B25/00—Models for purposes not provided for in G09B23/00, e.g. full-sized devices for demonstration purposes
Similar Documents
Publication | Publication Date | Title |
---|---|---|
White et al. | Assessing induced seismicity risk at CO2 storage projects: Recent progress and remaining challenges | |
US20210264262A1 (en) | Physics-constrained deep learning joint inversion | |
RamaRao et al. | Pilot point methodology for automated calibration of an ensemble of conditionally simulated transmissivity fields: 1. Theory and computational experiments | |
EP3535607B1 (en) | Seismic data processing artificial intelligence | |
Hashash et al. | Novel approach to integration of numerical modeling and field observations for deep excavations | |
Foroud et al. | Assisted history matching using artificial neural network based global optimization method–Applications to Brugge field and a fractured Iranian reservoir | |
US20100138202A1 (en) | System and method of grid generation for discrete fracture modeling | |
CN109478208A (en) | The iteration of integrated data and process integration for oil exploration and production assessment and repeatable workflow | |
Király‐Proag et al. | Validating induced seismicity forecast models—Induced seismicity test bench | |
EP3555798A1 (en) | Subsurface modeler workflow and tool | |
EP3987478A1 (en) | Field development planning based on deep reinforcement learning | |
Omosebi et al. | Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage | |
US20220178228A1 (en) | Systems and methods for determining grid cell count for reservoir simulation | |
WO2022153984A1 (en) | Learning data generation method, model generation method, and learning data generation device | |
Siriwardane | Overview of SMART Initiative | |
Mishra | Machine learning applications in subsurface energy resource management: state of the art and future prognosis | |
US10145984B2 (en) | System, method and computer program product for smart grouping of seismic interpretation data in inventory trees based on processing history | |
RU2477528C2 (en) | Interactive automated training system | |
Thomas et al. | NRAP Recommended Practices for Containment Assurance and Leakage Risk Quantification | |
Landinez et al. | First steps on modelling wave propagation in isotropic-heterogeneous media: Numerical simulation of P–SV waves | |
Bromhal et al. | The SMART Initiative: Applying Machine Learning to Enable Efficient and Effective Real-Time Decisions for Geological Carbon Storage Operations | |
RU107875U1 (en) | INTERACTIVE AUTOMATED LEARNING SYSTEM | |
Hwang et al. | Advancing solid earth system science through high-performance computing | |
Auriol | Contributions to the robust stabilization of networks of hyperbolic systems | |
Krantz et al. | Learning from the 2013 3-D interpretation Hedberg conference: How geoscientists see 3-D |