[go: up one dir, main page]

Moreno et al., 2025 - Google Patents

Reducing density uncertainty in iron ore deposits: Taking advantage of the density and Fe grades correlation aiming to more accurate models

Moreno et al., 2025

Document ID
13971239988425071219
Author
Moreno W
Bassani M
Marques D
Costa J
Publication year
Publication venue
Resources Policy

External Links

Snippet

This study addresses the critical role of density in the economic evaluation of mineral deposits and how uncertain could be the density models without considering the available secondary information. Most mining companies subjectively determine sample quantity and …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • 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
    • G01V1/306Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • 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
    • 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
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • 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
    • G01V5/00Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity
    • G01V5/04Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity specially adapted for well-logging
    • G01V5/08Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity specially adapted for well-logging using primary nuclear radiation sources or X-rays
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/61Analysis by combining or comparing a seismic data set with other data
    • 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
    • G01V3/00Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/12Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
    • 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
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/26Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
    • G01N33/28Oils, i.e. hydrocarbon liquids
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation

Similar Documents

Publication Publication Date Title
Pinheiro et al. Geostatistical simulation to map the spatial heterogeneity of geomechanical parameters: A case study with rock mass rating
Cornah et al. Comparison of three geostatistical approaches to quantify the impact of drill spacing on resource confidence for a coal seam (with a case example from Moranbah North, Queensland, Australia)
Doostmohammadi et al. Geostatistical modeling of uniaxial compressive strength along the axis of the Behesht-Abad tunnel in Central Iran
Molayemat et al. The impact of the compositional nature of data on coal reserve evaluation, a case study in Parvadeh IV coal deposit, Central Iran
Tahernejad et al. Analyzing the effect of ore grade uncertainty in open pit mine planning; A case study of Rezvan iron mine, Iran
Goovaerts Geostatistical modeling of the spaces of local, spatial, and response uncertainty for continuous petrophysical properties
Moreno et al. Reducing density uncertainty in iron ore deposits: Taking advantage of the density and Fe grades correlation aiming to more accurate models
Ongarbayev et al. Anisotropic inverse distance weighting method: An innovative technique for resource modeling of vein-type deposits
Maxwell et al. Spatial modelling and classification of altered coal using random forest-based methods at Moatize Basin, Mozambique
Salarian et al. Improving the resource modeling results using auxiliary variables in estimation and simulation methods
Babak et al. Accounting for non-exclusivity in sequential indicator simulation of categorical variables
Madani et al. Joint simulation of cross-correlated ore grades and geological domains: an application to mineral resource modeling
Lawal et al. Uncertainty-aware reservoir permeability prediction using gaussian processes regression and nmr measurements
Larrondo et al. Grade estimation in multiple rock types using a linear model of coregionalization for soft boundaries
Ekolle Essoh et al. Assessing the Uncertainty in Lithology, Grades and Recoverable Resources in an Iron Deposit in Southern Cameroon
Boyd The application of geostatistical methods for the quantification of multiple-scale uncertainty due to aleatory geologic variability
Pinto Advances in data spacing and uncertainty
Krasnikov et al. Consideration of Elastic Properties and Stresses Anisotropy in Fracturing Planning
Aduko Compositing High Resolution Assay Data for Mineral Resources
Tokoglu Comparative analysis of 3D domain modelling alternatives: implications for mineral resource estimates
Hlajoane Joint Simulation of Continuous and Categorical Variables for Mineral Resource Modeling and Recoverable Reserves Calculation
Kim et al. Determining Coal Thickness Through Compositional Kriging: An Approach Based on Geostatistics.
Tembo The Application of Dynamic Anisotropy in the Kriging Estimation Process to Improve Resource Estimation on a Folded Undulating Stratiform Copper Deposit
Wambeke et al. An integrated approach to simulate and validate orebody realizations with complex trends: A case study in heavy mineral sands
Dinda et al. Recoverable reserve estimation using non-stationary and non-Gaussian copula-based simulation model