[go: up one dir, main page]

Al-Abduljabbar et al., 2020 - Google Patents

Application of artificial neural network to predict the rate of penetration for S-shape well profile

Al-Abduljabbar et al., 2020

View PDF
Document ID
4790392011590874475
Author
Al-Abduljabbar A
Gamal H
Elkatatny S
Publication year
Publication venue
Arabian Journal of Geosciences

External Links

Snippet

The rate of penetration (ROP) is defined as the required speed to break the drilled rock by the bit action. The existing established models for estimating the rate of penetration include the basic mathematical correlation that have many limitations. The objective of this paper is …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B44/00Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B41/00Equipment or details not covered by groups E21B15/00 - E21B40/00
    • E21B2041/0028Fuzzy logic, artificial intelligence, neural networks, or the like
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E21B49/08Obtaining fluid samples or testing fluids, in boreholes or wells
    • 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
    • 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
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B41/00Equipment or details not covered by groups E21B15/00 - E21B40/00
    • E21B41/0092Methods relating to program engineering, design or optimisation
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B4/00Drives used in the borehole
    • E21B4/02Fluid rotary type drives
    • 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
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00Survey of boreholes or wells
    • EFIXED CONSTRUCTIONS
    • E21EARTH DRILLING; MINING
    • E21BEARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B21/00Methods or apparatus for flushing boreholes, e.g. by use of exhaust air from motor
    • E21B21/08Controlling or monitoring pressure or flow of drilling fluid, e.g. automatic filling of boreholes, automatic control of bottom pressure
    • 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

Similar Documents

Publication Publication Date Title
Al-Abduljabbar et al. Application of artificial neural network to predict the rate of penetration for S-shape well profile
Moraveji et al. Drilling rate of penetration prediction and optimization using response surface methodology and bat algorithm
Alsaihati et al. Use of machine learning and data analytics to detect downhole abnormalities while drilling horizontal wells, with real case study
Abdelgawad et al. New approach to evaluate the equivalent circulating density (ECD) using artificial intelligence techniques
Shahani et al. Predictive modeling of drilling rate index using machine learning approaches: LSTM, simple RNN, and RFA
Wang et al. Application of real-time field data to optimize drilling hydraulics using neural network approach
Lashari et al. Drilling performance monitoring and optimization: a data-driven approach
Elkatatny Real-time prediction of rate of penetration while drilling complex lithologies using artificial intelligence techniques
Al-AbdulJabbar et al. Artificial neural network model for real-time prediction of the rate of penetration while horizontally drilling natural gas-bearing sandstone formations
Ashena et al. Drilling parameters optimization using an innovative artificial intelligence model
Elkatatny Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network
Gowida et al. Application of artificial neural network to predict formation bulk density while drilling
Youcefi et al. Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm
Saadeldin et al. Intelligent model for predicting downhole vibrations using surface drilling data during horizontal drilling
Wang et al. Drilling hydraulics optimization using neural networks
Rooki et al. Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network
Chandrasekaran et al. Drilling efficiency improvement and rate of penetration optimization by machine learning and data analytics
Ren et al. Predicting rate of penetration of horizontal drilling by combining physical model with machine learning method in the China Jimusar Oil Field
Abdulmalek et al. New approach to predict fracture pressure using functional networks
Batruny et al. Drilling in the digital age: an aproach to optimizing ROP using machine learning
Ahmed et al. Fracture pressure prediction using surface drilling parameters by artificial intelligence techniques
Zanjani et al. Data-driven hydrocarbon production forecasting using machine learning techniques
Höhn et al. Framework for automated generation of real-time rate of penetration models
Pei et al. Wide and deep cross network for the rate of penetration prediction
Alsaihati et al. Determining severity of lateral and torsional downhole vibrations while drilling surface holes using three machine learning techniques