[go: up one dir, main page]

Alsabaa et al., 2021 - Google Patents

New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks

Alsabaa et al., 2021

Document ID
10078832811727585674
Author
Alsabaa A
Gamal H
Elkatatny S
Abdulraheem A
Publication year
Publication venue
Flow Measurement and Instrumentation

External Links

Snippet

The rheological properties of the drilling fluid are crucial to the success of the drilling project. The traditional mud experiments normally performed by the mud engineers provide rheological data with a small resolution. Monitoring higher-resolution rheological properties …
Continue reading at www.sciencedirect.com (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
    • 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
    • E21B49/087Well testing, e.g. testing for reservoir productivity or formation parameters
    • 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
    • E21B47/12Means for transmitting measuring-signals or control signals from the well to the surface or from the surface to the well, e.g. for logging while drilling
    • E21B47/14Means for transmitting measuring-signals or control signals from the well to the surface or from the surface to the well, e.g. for logging while drilling using acoustic waves
    • 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
    • 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
    • E21B47/06Measuring temperature or pressure
    • E21B47/065Measuring temperature
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/26Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
    • G01N33/28Oils, i.e. hydrocarbon liquids
    • G01N33/2823Oils, i.e. hydrocarbon liquids raw oil, drilling fluid or polyphasic mixtures
    • 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
    • E21B49/005Testing the nature of borehole walls or the formation by using drilling mud or cutting data
    • 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
    • E21B47/10Locating fluid leaks, intrusions or movements
    • 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

Similar Documents

Publication Publication Date Title
Alsabaa et al. New correlations for better monitoring the all-oil mud rheology by employing artificial neural networks
Elkatatny Real-time prediction of rheological parameters of KCL water-based drilling fluid using artificial neural networks
Ahmadi et al. An accurate model to predict drilling fluid density at wellbore conditions
Elkatatny et al. Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box)
Al-Azani et al. Real time prediction of the rheological properties of oil-based drilling fluids using artificial neural networks
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
Alsabaa et al. Real-time prediction of rheological properties of all-oil mud using artificial intelligence
US9117169B2 (en) Methods and apparatuses for modeling shale characteristics in wellbore servicing fluids using an artificial neural network
Rooki et al. Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network
AU5279702A (en) Method for determining real-time downhole mud properties while drilling
Chen et al. Early gas kick detection-inversion-control integrated system: The significance of applications of managed pressure drilling: A review
Ameur-Zaimeche et al. Real-time porosity prediction using gas-while-drilling data and machine learning with reservoir associated gas: Case study for Hassi Messaoud field, Algeria
Gouda et al. Prediction of the rheological properties of invert emulsion mud using an artificial neural network
Gul Machine learning applications in drilling fluid engineering: A review
Avcı An artificial neural network approach for the prediction of water-based drilling fluid rheological behaviour
Al-Obaidi et al. Artificial intelligent for real-time prediction of rheological drilling mud properties
CN119047343B (en) Wellbore stability control method, device, equipment and storage medium for dynamically adjusting drilling fluid ratio
Gamal et al. Artificial Neural Network Model for Predicting the Equivalent Circulating Density from Drilling Parameters
Deng et al. Prediction of water-in-oil emulsion drilling fluids rheological properties based on GPR-Bagging ensemble learning
Wei et al. Data Assimilation-Based Real-Time Estimation of Downhole Gas Influx Rate and Void Fraction Distribution in a Drilling Riser
Johnson et al. Real-time friction factor monitoring: characterization of drag reduction in polymer-based fluids
Anifowose et al. Contributions of machine learning to quantitative and real-time mud gas data analysis: A critical review
Zamora et al. The top 10 reasons to rethink hydraulics and rheology
Abdelaal et al. Viscometer readings prediction of flat rheology drilling fluids using adaptive neuro-fuzzy inference system
Patidar et al. Enhancing PVT property predictions for black oil reservoirs through the application of supervised machine learning techniques