Geomodelling TRIZ Cases
- Fully Convolutional Neural Network Magnetotelluric Inversion
Pre-trained synthetic data enables the network to resolve local extremum issues and reduce operation time while enhancing fitting precision.
- Variable Discretization for Reservoir Flow Simulation
A variable discretization method combines finite element and finite difference techniques within a single computational framework.
- Automated Well Placement Planning in Reservoir Simulation
Reservoir simulator ranks candidate well locations by predicted production rate to reduce manual planning time.
- Porosity Prediction via Variable Compression Index
A sediment porosity model uses a variable compression index to adapt compaction behavior across different void ratio ranges.
- Seismic Data Inversion With Perturbation-Based Uncertainty Analysis
Processes 4D seismic data through multiple model perturbations and statistical analysis to quantify uncertainties in reservoir property estimates.
- Machine Learning Reservoir Model Switching
A machine learning classifier selects reservoir models based on geomechanical simulation results.
- Geophysical Inversion via Convolutional Neural Networks
Convolutional neural networks extract subsurface physical properties from geophysical survey data, reducing computational time and improving accuracy.
- Continental Slope Foot Point Recognition via Terrain Grid Derivation
Automated terrain grid processing extracts continental slope foot points through multi-stage derivative analysis and geometric simplification.
- Non-linear Petrofacies Identification via Cross-Plot Partitioning
Automated cross-plot partitioning defines non-linear petrofacies boundaries using data frequency analysis for systematic reservoir characterization.
- Seismic Inference System for Geocontextual Object Identification
Inference system analyzes multiple seismic attributes using geocontextual modeling rules to automate identification of complex geo-objects.
- Correcting Relative Permeability for Accurate Water Breakthrough Prediction
A method corrects initial relative permeability data using viscosity and flood-front saturation points to simulate water breakthrough time accurately.
- Automated Seismic Interpretation via AVA Clustering
Machine learning identifies seismic facies from pre-stack data using AVA clusters to generate digital images.
- Thumbnail Extraction for Multipoint Geostatistical Subsoil Modeling
Extracting facies-based thumbnails from training images constrains multipoint geostatistical reservoir simulations.
- Geological Database Signatures for Hydrocarbon Exploration
Parameter signatures enable automated identification of analogous geological formations in hydrocarbon exploration.
- 3D Reservoir Model via Process-Aided Multiple-Point Simulation
Process-based modeling extracts depositional statistics to construct multiple unconditional geologic models for reservoir simulation.
- 3D Angle Gathers via Anti-Leakage Fourier Transform
Anti-leakage Fourier transform converts seismic wavefields into the wavenumber domain for precise angle calculation.
- Seismic Inversion via Spherical Elastic Parameter Constraints
Spherical projection of elastic parameters resolves non-uniqueness in 4D seismic inversion by applying distance penalties to ensure geological consistency.
- Combined Physical Numerical Subterranean Simulation
Calibrates numerical models using limited core samples to resolve high-temperature reliability issues.
- Automated Rock Physics Modeling via Inverse Problem Solutions
Automated rock physics modeling determines optimal parameters through iterative inversion to resolve accuracy versus complexity trade-offs.
- 3D Inversion of Deep Resistivity Measurements
Constrained nonlinear transformations invert 3D subsurface formation models to determine horizontal and vertical resistivity parameters.
- Sand Production Prediction via Critical Drawdown Pressure
Trained model determines critical drawdown pressure from petrophysical data to predict sand mobilization and optimize hydrocarbon recovery.