Yasin et al., 2024 - Google Patents
Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirsYasin et al., 2024
View HTML- Document ID
- 11678896990237684915
- Author
- Yasin Q
- Ding Y
- Du Q
- Thanh H
- Liu B
- Publication year
- Publication venue
- Geomechanics and Geophysics for Geo-Energy and Geo-Resources
External Links
Snippet
Geothermal energy is a sustainable energy source that meets the needs of the climate crisis and global warming caused by fossil fuel burning. Geothermal resources are found in complex geological settings, with faults and interconnected networks of fractures acting as …
- 238000004364 calculation method 0 title abstract description 8
Classifications
-
- 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
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
-
- 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
- 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
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6163—Electromagnetic
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
- G01V2210/6248—Pore pressure
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/66—Subsurface modeling
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
- G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V11/00—GEOPHYSICS; 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V5/00—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V8/00—Prospecting or detecting by optical means
- G01V8/02—Prospecting
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Miah et al. | Machine learning approach to model rock strength: prediction and variable selection with aid of log data | |
| Xu et al. | When petrophysics meets big data: What can machine do? | |
| Liu et al. | Petrophysical characteristics and log identification of lacustrine shale lithofacies: A case study of the first member of Qingshankou Formation in the Songliao Basin, Northeast China | |
| Avanzini et al. | Lithologic and geomechanical facies classification for sweet spot identification in gas shale reservoir | |
| Ashraf et al. | Reservoir rock typing assessment in a coal-tight sand based heterogeneous geological formation through advanced AI methods | |
| Wang et al. | Improved permeability prediction based on the feature engineering of petrophysics and fuzzy logic analysis in low porosity–permeability reservoir | |
| Ashraf et al. | Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock typing of depositional facies in heterogeneous rocks | |
| Yasin et al. | Fault and fracture network characterization using seismic data: a study based on neural network models assessment | |
| Yasin et al. | Fault and fracture network characterization using soft computing techniques: application to geologically complex and deeply-buried geothermal reservoirs | |
| Hajibolouri et al. | Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches | |
| Al-Mudhafar et al. | Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications | |
| Wang et al. | Quantitative evaluation of unconsolidated sandstone heavy oil reservoirs based on machine learning | |
| Davari et al. | Comprehensive input models and machine learning methods to improve permeability prediction | |
| Zhao et al. | Quantitative classification and prediction of diagenetic facies in tight gas sandstone reservoirs via unsupervised and supervised Machine learning models: Ledong Area, Yinggehai Basin | |
| Deng et al. | Deep learning for predicting porosity in ultra-deep fractured vuggy reservoirs from the Shunbei oilfield in Tarim Basin, China | |
| Das et al. | Identification of lithofacies from well log data in the upper Assam basin using machine learning techniques | |
| Rushing et al. | An Integrated Work-Flow Model to Characterize Unconventional Gas Resources: Part II—Formation Evaluation and Reservoir Modeling | |
| Sun et al. | Real-time updating method of local geological model based on logging while drilling process | |
| Khanjar | Applications of Machine Learning in Sweet-Spots Identification: A Review | |
| Garia et al. | Mapping petrophysical properties with seismic inversion constrained by laboratory based rock physics model | |
| Feng et al. | Simultaneous prediction of porosity, saturation, and lithofacies from seismic data via multi-task deep learning | |
| Huang et al. | Quantitative analysis of the main controlling factors of oil saturation variation | |
| Fang et al. | Principal Slip Zone determination in the Wenchuan earthquake Fault Scientific Drilling project-hole 1: considering the Bayesian discriminant function | |
| Abbas et al. | Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs | |
| Mohammadpour et al. | Effect of spatial variability of downhole geophysical logs on machine learning exercises |