Al-Mudhafar, 2016 - Google Patents
Incorporation of bootstrapping and cross-validation for efficient multivariate facies and petrophysical modelingAl-Mudhafar, 2016
- Document ID
- 10897262041029010197
- Author
- Al-Mudhafar W
- Publication year
- Publication venue
- SPE Rocky Mountain Petroleum Technology Conference/Low-Permeability Reservoirs Symposium
External Links
Snippet
An integrated multivariate statistics procedure was adopted for the accurate Lithofacies classification prediction to be incorporated with well log attributes into core permeability modeling. Logistic Boosting Regression and Generalized Linear Modeling were adopted for …
- 238000002790 cross-validation 0 title abstract description 22
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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- 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
- 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
- G01V2210/66—Subsurface modeling
- G01V2210/665—Subsurface modeling using geostatistical modeling
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Al-Mudhafar | Incorporation of bootstrapping and cross-validation for efficient multivariate facies and petrophysical modeling | |
Zhong et al. | A deep-learning-based approach for reservoir production forecast under uncertainty | |
Al-Mudhafar | Integrating machine learning and data analytics for geostatistical characterization of clastic reservoirs | |
J Al-Mudhafar | Integrating lithofacies and well logging data into smooth generalized additive model for improved permeability estimation: Zubair formation, South Rumaila oil field | |
Li et al. | Can we improve the spatial predictions of seabed sediments? A case study of spatial interpolation of mud content across the southwest Australian margin | |
Kabir et al. | Developing new fields using probabilistic reservoir forecasting | |
Khodabakhshi et al. | A Bayesian mixture‐modeling approach for flow‐conditioned multiple‐point statistical facies simulation from uncertain training images | |
Bize-Forest et al. | Using machine-learning for depositional facies prediction in a complex carbonate reservoir | |
Abbas et al. | Clustering analysis and flow zone indicator for electrofacies characterization in the upper shale member in Luhais oil field, southern Iraq | |
Al-Mudhafar | Integrating electrofacies and well logging data into regression and machine learning approaches for improved permeability estimation in a carbonate reservoir in a giant southern Iraqi oil field | |
Agbalaka et al. | Joint updating of petrophysical properties and discrete facies variables from assimilating production data using the EnKF | |
Cai et al. | Development of a powerful data-analysis tool using nonparametric smoothing models to identify drillsites in tight shale reservoirs with high economic potential | |
Chen et al. | Ensemble-level upscaling for efficient estimation of fine-scale production statistics | |
Cheong et al. | Experimental design and analysis methods for assessing volumetric uncertainties | |
Al-Mudhafar et al. | Incorporating lithofacies classification and well logs into statistical learning algorithms for comparative multisource permeability modelling | |
Sharma et al. | Classification of oil and gas reservoirs based on recovery factor: a data-mining approach | |
Al-Farisi et al. | Revelation of carbonate rock typing–the resolved gap | |
Zeiza et al. | Reservoir characterization and 3D architecture of multi-scale vugular pore systems in carbonate reservoirs | |
Al-Mudhafar | Applied geostatistical reservoir characterization in R: review and implementation of permeability estimation modeling and prediction algorithms-Part II | |
Santoso et al. | Multi-Fidelity Bayesian Approach for History Matching in Reservoir Simulation | |
Al-Mudhafar et al. | Integrating Designed Simulations for Bayesian Identification of Geological Controls on the Performance of a Multilayer Heterogeneous Sandstone Oil Reservoir: Applied on the CO2-Assisted Gravity Drainage Process | |
D'Windt et al. | Bayesian Based Approach for Hydraulic Flow Unit Identification and Permeability Prediction: A Field Case Application in a Tight Carbonate Reservoir | |
Coutinho et al. | Conditioning multilayered geologic models to well-test and production-logging data using the ensemble Kalman filter | |
Hamdi et al. | Calibrating multi-point geostatistical models using pressure transient data | |
Chang et al. | Facies parameterization and estimation for complex reservoirs-the Brugge field |