Zhang et al., 2022 - Google Patents
Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging‐Based Ensemble and Transfer Learnings: A Case Study of …Zhang et al., 2022
View PDF- Document ID
- 7692734649204750723
- Author
- Zhang S
- Gu Y
- Gao Y
- Wang X
- Zhang D
- Zhou L
- Publication year
- Publication venue
- Geofluids
External Links
Snippet
From a general review, most petrophysical models applied for the conventional logging interpretation imply that porosity, permeability, or water saturation mathematically have a linear or nonlinear relationship with well logs, and then arguing the prediction of these three …
- 230000013016 learning 0 title abstract description 98
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- 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
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
-
- 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
-
- 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
- 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
-
- 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
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Das et al. | Petrophysical properties prediction from prestack seismic data using convolutional neural networks | |
| Kardani et al. | Predicting permeability of tight carbonates using a hybrid machine learning approach of modified equilibrium optimizer and extreme learning machine | |
| Abbas et al. | Improving permeability prediction in carbonate reservoirs through gradient boosting hyperparameter tuning | |
| Ma et al. | An efficient spatial-temporal convolution recurrent neural network surrogate model for history matching | |
| Sheykhinasab et al. | Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms | |
| Zhou et al. | Fast prediction of reservoir permeability based on embedded feature selection and LightGBM using direct logging data | |
| Tian et al. | Deep learning assisted well log inversion for fracture identification | |
| Yang et al. | Deep-learning missing well-log prediction via long short-term memory network with attention-period mechanism | |
| Ogunkunle et al. | Artificial intelligence model for predicting geomechanical characteristics using easy-to-acquire offset logs without deploying logging tools | |
| Hu et al. | Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques | |
| Gu et al. | Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs | |
| He et al. | Porosity prediction of tight reservoir rock using well logging data and machine learning | |
| Al-Mudhafar et al. | Integration of electromagnetic, resistivity-based and production logging data for validating lithofacies and permeability predictive models with tree ensemble algorithms in heterogeneous carbonate reservoirs | |
| Gohari Nezhad et al. | Enhancing water saturation predictions from conventional well logs in a carbonate gas reservoir with a hybrid CNN-LSTM model | |
| Cao et al. | Acoustic log prediction on the basis of kernel extreme learning machine for wells in GJH survey, Erdos Basin | |
| Davari et al. | Comprehensive input models and machine learning methods to improve permeability prediction | |
| Krishna et al. | Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models | |
| Surachman et al. | Acoustic impedance inversion via voting stacked regression (VStaR) algorithms | |
| Zhang et al. | MS-CGAN: Fusion of conditional generative adversarial networks and multi-scale spatio-temporal features for lithology identification | |
| Zhang et al. | Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging‐Based Ensemble and Transfer Learnings: A Case Study of Sandy‐Mud Reservoirs | |
| Baouche et al. | Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs: a case study of Saharan field | |
| Gu et al. | A smart predictor used for lithologies of tight sandstone reservoirs: a case study of member of Chang 4+ 5, Jiyuan Oilfield, Ordos Basin | |
| Choubineh et al. | Deep ensemble learning for high-dimensional subsurface fluid flow modeling | |
| Fan et al. | Logging-data-driven lithology identification in complex reservoirs: an example from the Niuxintuo block of the Liaohe oilfield | |
| Osogba | Machine Learning for Subsurface Data Analysis: Applications in Outlier Detection, Signal Synthesis and Core & Completion Data Analysis |