Saint Antonin, 2024 - Google Patents
Dynamic Model History Matching and Testing in Petroleum Reservoir SimulationSaint Antonin, 2024
View HTML- Document ID
- 2334671442599144170
- Author
- Saint Antonin J
- Publication year
External Links
Snippet
When transition from static to dynamic reservoir modeling, historical field performance serves as a crucial benchmark. Unfortunately, freshly constructed geological models often fall short of accurately reproducing this historical behavior. To bridge this gap, the industry …
- 238000004088 simulation 0 title abstract description 43
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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- 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
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
- G01V2210/6248—Pore pressure
-
- 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
- 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
- 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
- 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
- G01N33/26—Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
- G01N33/28—Oils, i.e. hydrocarbon liquids
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhong et al. | A deep-learning-based approach for reservoir production forecast under uncertainty | |
Amer et al. | An ROP predictive model in nile delta area using artificial neural networks | |
Navrátil et al. | Accelerating physics-based simulations using end-to-end neural network proxies: An application in oil reservoir modeling | |
Srinivasan et al. | A machine learning framework for rapid forecasting and history matching in unconventional reservoirs | |
US8874419B2 (en) | Method of developing a petroleum reservoir from a facies map construction | |
Kiærr et al. | Evaluation of a data-driven flow network model (FlowNet) for reservoir prediction and optimization | |
Van den Hof et al. | Recent developments in model-based optimization and control of subsurface flow in oil reservoirs | |
Chen et al. | Integration of principal-component-analysis and streamline information for the history matching of channelized reservoirs | |
Sarma et al. | Redistribution of steam injection in heavy oil reservoir management to improve EOR economics, powered by a unique integration of reservoir physics and machine learning | |
US20140019108A1 (en) | Method for exploiting a geological reservoir from a reservoir model matched by the computation of an analytical law of conditional distribution of uncertain parameters of the model | |
Razak et al. | History matching with generative adversarial networks | |
Song et al. | Potential for vertical heterogeneity prediction in reservoir basing on machine learning methods | |
Sanei et al. | Applied machine learning-based models for predicting the geomechanical parameters using logging data | |
US8942967B2 (en) | Method for real-time reservoir model updating from dynamic data while keeping the coherence thereof with static observations | |
Cornelio et al. | Physics-assisted transfer learning for production prediction in unconventional reservoirs | |
Eidsvik et al. | Simulation–regression approximations for value of information analysis of geophysical data | |
Pyrcz et al. | Uncertainty in reservoir modeling | |
Hossain et al. | Porosity prediction and uncertainty estimation in tight sandstone reservoir using non-deterministic XGBoost | |
Elharith et al. | Integrated modeling of a complex oil rim development scenario under subsurface uncertainty | |
Al-Shamma et al. | History matching of the Valhall field using a global optimization method and uncertainty assessment | |
Maniglio et al. | Physics Informed Neural Networks Based on a Capacitance Resistance Model for Reservoirs Under Water Flooding Conditions | |
Hicks et al. | Identifying and quantifying significant uncertainties in basin modeling | |
Saint Antonin | Dynamic Model History Matching and Testing in Petroleum Reservoir Simulation | |
Saint Antonin | Overview of History-Matching Approaches and Testing in Reservoir Simulation | |
Hutahaean | Multi-objective methods for history matching, uncertainty prediction and optimisation in reservoir modelling |