[go: up one dir, main page]

Ben et al., 2020 - Google Patents

Real-time hydraulic fracturing pressure prediction with machine learning

Ben 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 …
Continue reading at onepetro.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • 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
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V99/00Subject 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