Pipe Deposition Interpretation Using Acoustic Flow Profile Analysis
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Solution Overview
Problem
Existing methods for identifying and managing deposits in well and flowline systems are costly, time-consuming, and impractical for on-site deployment, leading to delayed identification and ineffective treatments that can cause significant revenue loss and operational inefficiencies.
Innovation Solution
A non-intrusive deposition measurement system using neural networks and multivariate regression analysis to analyze pressure data from sensors, allowing for quick and efficient identification of deposit locations and properties without the need for intrusive devices, utilizing acoustic or pressure waves to characterize deposits and predict their behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional intrusive methods are used to identify deposits, then measurement precision may be improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces complex mechanical intrusive measurement devices with acoustic wave-based detection. Acoustic waves are transmitted through the flowline and interactions with deposits are analyzed to identify deposit presence, location, and properties without physical intrusion into the flowline
Solution Approach 2:
The patent uses acoustic waves as an intermediary medium to detect deposits. Instead of direct contact measurement, acoustic waves serve as the mediator that carries information about deposit characteristics from the flowline interior to external sensors for analysis
2Measurement precision
If traditional deposit identification methods are used, then measurement precision may be improved, but loss of time increases due to delayed identification
Solution Approach 1:
The patent enables continuous monitoring of deposits through ongoing acoustic wave transmission and analysis. This continuous detection capability allows for real-time identification of deposit formation and growth, eliminating delays associated with periodic intrusive inspections
Solution Approach 2:
The patent detects deposits at early stages of formation using acoustic wave analysis before they grow large enough to cause blockages. This preliminary detection enables proactive maintenance scheduling and prevents operational disruptions
3Measurement precision
If intrusive devices are deployed for deposit measurement, then measurement precision may be improved, but ease of operation deteriorates due to on-site deployment difficulties
Solution Approach 1:
The patent replaces intrusive mechanical devices that require physical installation into the flowline with external acoustic detection equipment. This substitution eliminates the complex deployment process while maintaining deposit measurement capabilities through non-contact acoustic wave analysis
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables timely and cost-effective identification of deposits, reducing the risk of pipeline blockages and operational disruptions by providing precise data for targeted cleaning and maintenance, thereby enhancing operational efficiency and reducing downtime.
Implementation Method 1
utilizing acoustic or pressure waves to characterize deposits and predict their behavior
Implementation Method 2
analyze pressure data from sensors, allowing for quick and efficient identification of deposit locations and properties
Data Source
AI summary
Disclosed are systems, apparatuses, methods, and computer readable medium for modeling depositions within a pipe. A method includes: building a predictive model of an interior of a pipe based on legacy data observations; receiving flowline data from a sensor indicating a flow profile within the pipe; analyzing the flowline data using the predictive model; outputting, from the predictive model, data representing a change in the flow profile, wherein the change in the flow profile indicates a difference between the legacy data observations and the flowline data; and rendering a representation of the data representing the change in the flow profile.