Ben et al., 2020 - Google Patents
Real-time hydraulic fracturing pressure prediction with machine learningBen et al., 2020
- Document ID
- 2028164613096619803
- Author
- Ben Y
- Perrotte M
- Ezzatabadipour M
- Ali I
- Sankaran S
- Harlin C
- Cao D
- Publication year
- Publication venue
- SPE hydraulic fracturing technology conference and exhibition
External Links
Snippet
During hydraulic fracturing jobs, engineers must monitor the wellhead pressure and adjust the pumping schedule in real time to avoid screenout, optimize the proppant and fluid amounts, and minimize cost. In this paper, we use machine learning to predict wellhead …
- 238000010801 machine learning 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- 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
- G01V99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ben et al. | Real-time hydraulic fracturing pressure prediction with machine learning | |
| EP2951720B1 (en) | Production analysis and/or forecasting methods, apparatus, and systems | |
| US10275715B2 (en) | Control variable determination to maximize a drilling rate of penetration | |
| Cullick et al. | Optimizing multiple-field scheduling and production strategy with reduced risk | |
| US8504341B2 (en) | Methods, systems, and computer readable media for fast updating of oil and gas field production models with physical and proxy simulators | |
| US20070179767A1 (en) | Methods, systems, and computer-readable media for fast updating of oil and gas field production models with physical and proxy simulators | |
| Amirian et al. | Data-driven modeling approach for recovery performance prediction in SAGD operations | |
| Nande | Application of machine learning for closure pressure determination | |
| Wang et al. | Field application of deep learning for flow rate prediction with downhole temperature and pressure | |
| Xu et al. | Shale Gas Production Forecasting with Well Interference Based on Spatial-Temporal Graph Convolutional Network | |
| Mohd Razak et al. | Integrating deep learning and physics-based models for improved production prediction in unconventional reservoirs | |
| Xu et al. | Dynamic Real-Time Production Forecasting Model for Complex Subsurface Flow Systems with Variable Length Input Sequences | |
| Khan et al. | Physics-Informed Machine Learning for Hydraulic Fracturing—Part II: The Transfer Learning Experiment | |
| Aliyev et al. | A novel application of artificial neural networks to predict rate of penetration | |
| Thabet et al. | Application of Machine Learning and Deep Learning to Predict Production Rate of Sucker Rod Pump Wells | |
| Singh | Permeability prediction using artificial neural network (ANN): a case study of Uinta Basin | |
| Bahaa et al. | Soft computation application: Utilizing artificial neural network to predict the fluid rate and bottom hole flowing pressure for gas-lifted oil wells | |
| Maniglio et al. | Physics informed neural networks based on a capacitance resistance model for reservoirs under water flooding conditions | |
| Aljubran et al. | Surrogate-Based Prediction and Optimization of Multilateral Inflow Control Valve Flow Performance with Production Data | |
| Nustes Andrade et al. | Real-time analysis and forecasting of the microseismic cloud size: Physics-based models versus machine learning | |
| Gopa et al. | Cognitive analytical system based on data-driven approach for mature reservoir management | |
| Addagalla et al. | Using mesophase technology to remove and destroy the oil-based mud filter cake in wellbore remediation applications-case histories, Saudi Arabia | |
| Chen et al. | Reservoir recovery estimation using data analytics and neural network based analogue study | |
| US11599955B2 (en) | Systems and methods for evaluating and selecting completion equipment using a neural network | |
| Li et al. | Real-time wellhead pressure prediction: An integration of deep learning and physical modeling |