Nothing Special   »   [go: up one dir, main page]

Noufal et al., 2021 - Google Patents

Machine learning in computer vision software for geomechanics modeling

Noufal et al., 2021

Document ID
1241578918523312977
Author
Noufal A
Sreekantan J
Belmeskine R
Amri M
Benaichouche A
Publication year
Publication venue
Abu Dhabi International Petroleum Exhibition and Conference

External Links

Snippet

Abstract AI-GEM (Artificial Intelligence of Geomechanics Earth Modelling) tool aims to detect the geomechanical features, especially the elastic parameters and stresses. Characterizing the wellbore instability issues is one of the factors increases cost of drilling and creating an …
Continue reading at onepetro.org (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/62Physical property of subsurface
    • G01V2210/624Reservoir parameters
    • G01V2210/6248Pore pressure
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30Analysis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/66Subsurface modeling
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V99/00Subject matter not provided for in other groups of this subclass
    • G01V99/005Geomodels or geomodelling, not related to particular measurements
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38Processing data, e.g. for analysis, for interpretation, for correction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G01V1/44Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
    • G01V1/48Processing data
    • G01V1/50Analysing data
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V11/00GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V5/00Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/08Obtaining fluid samples or testing fluids, in boreholes or wells
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00Survey of boreholes or wells
    • E21B47/10Locating fluid leaks, intrusions or movements
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management

Similar Documents

Publication Publication Date Title
US8457940B2 (en) Model-consistent structural restoration for geomechanical and petroleum systems modeling
US8599643B2 (en) Joint structural dip removal
US20110246154A1 (en) Determine field fractures using geomechanical forward modeling
US20200095858A1 (en) Modeling reservoir permeability through estimating natural fracture distribution and properties
EP4042211B1 (en) Modeling reservoir permeability through estimating natural fracture distribution and properties
US11150371B2 (en) System and method for deriving reservoir stresses from 4D seismic data
Gabry et al. Application of Machine Learning Model for Estimating the Geomechanical Rock Properties Using Conventional Well Logging Data
Taipova et al. Verifying reserves opportunities with multi-well pressure pulse-code testing
US11650349B2 (en) Generating dynamic reservoir descriptions using geostatistics in a geological model
Sætrom et al. Consistent integration of drill-stem test data into reservoir models on a giant field offshore Norway
Noufal et al. Machine learning in computer vision software for geomechanics modeling
Halsey Computational sciences in the upstream oil and gas industry
Cornelio et al. A Machine Learning Approach for Predicting Rock Brittleness from Conventional Well Logs
Noohnejad et al. Integrated mechanical earth model and quantitative risk assessment to successful drilling
US8255816B2 (en) Modifying a magnified field model
Onyeji et al. Effective Real-time Pore Pressure Monitoring Using Well Events: Case Study of Deepwater West Africa-Nigeria
US9575195B2 (en) Detecting and quantifying hydrocarbon volumes in sub-seismic sands in the presence of anisotropy
Ling et al. Driving Deep Transient Testing with a Complete Digital Workflow–A Sustainable Exploration in Green Fields
Sayers et al. Determination of rock strength using advanced sonic log interpretation techniques
Al-Shemali et al. Subsurface Digital Models for Automated Drilling Risk Prediction in West Kuwait Jurassic Oilfields
Jones et al. The use of reservoir simulation in estimating reserves
Ling et al. Deep Transient Testing with a Complete Digital Workflow in South-East Asia Green Fields
Petrov et al. ML Driven Integrated Approach for Perforation Interval Selection Based on Advanced Borehole Images AI Assisted Interpretation
Saleh et al. Resistivity-based pore-pressure prediction—pitfalls and solutions
Katterbauer et al. A Novel Well Log Data Quality Prescriptive Framework for Enhancing Well Log Data Quality Interpretation