Nothing Special   »   [go: up one dir, main page]

Kolla et al., 2020 - Google Patents

Utilizing artificial intelligence for determining threshold sand rates from acoustic monitors

Kolla et al., 2020

Document ID
8896343086001934511
Author
Kolla S
Xu B
Nadeem A
Luo Q
Shirazi S
Sen S
Publication year
Publication venue
SPE Annual Technical Conference and Exhibition?

External Links

Snippet

Acoustic sand monitors are non-intrusive passive devices and they can be used to provide a warning when sand is being produced in pipelines and/or determine sand rates when calibrated. These devices are attractive to be installed on individual wells or manifolds to …
Continue reading at onepetro.org (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through the meter in a continuous flow
    • G01F1/74—Devices for measuring flow of a fluid or flow of a fluent solid material in suspension in another fluid
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/02—Investigating particle size or size distribution
    • 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
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • 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

Similar Documents

Publication Publication Date Title
Henrie et al. Pipeline leak detection handbook
Kora et al. Effects of high oil viscosity on slug liquid holdup in horizontal pipes
Talley et al. Characterization of horizontal air–water two-phase flow in a round pipe part II: Measurement of local two-phase parameters in bubbly flow
Salama Sand production management
McLaury et al. Distribution of sand particles in horizontal and vertical annular multiphase flow in pipes and the effects on sand erosion
Gokcal et al. Prediction of slug frequency for high-viscosity oils in horizontal pipes
Aliyu et al. An artificial neural network model for the prediction of entrained droplet fraction in annular gas-liquid two-phase flow in vertical pipes
Kolla et al. Utilizing artificial intelligence for determining threshold sand rates from acoustic monitors
Abdul-Majeed et al. Prediction of slug frequency for medium liquid viscosity two-phase flow in vertical, horizontal, and inclined pipes
Liu et al. Recognition of gas-liquid flow regimes in helically coiled tube using wire-mesh sensor and KNN algorithm
Giunta et al. Data driven smart monitoring for pipeline integrity assessment
Porter et al. Experimental investigation of induced vibrations in horizontal Gas-Liquid flow
Sharma et al. An evaluation of testing and modeling procedure for solids friction factor for fluidized dense-phase pneumatic conveying of fine powders
Vieira et al. Prediction of Threshold Sand Rates from Acoustic Monitors Using Artificial Intelligence
Mazumder S-bend erosion in particulated multiphase flow with air and sand
Shirazi et al. Sand monitor evaluation in multiphase flow
Cremaschi et al. Characterization of confidence in multiphase flow predictions
Odigie et al. Acoustic monitor threshold limits for sand detection in multiphase flow production system
Anumbe Experimental investigation of two-phase (gas/liquid) flow in intermediate sized, horizontal and inclined pipes
Du et al. Detecting gas–liquid two-phase flow pattern determinism from experimental signals with missing ordinal patterns
Brol et al. Self-affinity approach for the determination of flow regime in pipelines
Elgaddafi et al. Modeling Two-Phase Flow in Vertical and Deviated Wellbores Using Machine Learning Method
Chen et al. Effects of diameter and suction pipe opening position on excavation and suction rescue vehicle for gas-liquid two-phase position
Alssayh et al. Slug velocity measurement using acoustic emission technology
Streeton et al. Erosion and Sand Detection Testing of a Subsea Multiphase Meter with Integrated Erosion Monitoring System