Pipeline Discrepancy Detection for Corrected Branch Flow Rates
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Solution Overview
Problem
Current methods for estimating multiphase flow rates in oil and gas production systems face challenges due to measurement biases, model errors, and fluid phase changes, especially in transient pipeline models, where consistent measurements are not expected, leading to inaccuracies in flow rate estimation.
Innovation Solution
A method and system that detect discrepancy events in fluid pipelines by comparing empirical and simulated temperature and pressure measurements, using a filter bank for likelihood computations and a linear Bayesian minimum mean square error estimator to correct branch flow rates, accounting for factors like choke erosion and measurement biases, and estimating multiphase fluid flow rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If empirical measurements are used directly for flow rate estimation, then the measurement process is simple, but measurement biases and model errors lead to inaccurate flow rate estimates
Solution Approach 1:
The system segments the discrepancy detection process into multiple independent filters, each targeting specific types of discrepancy events (choke erosion, solids deposition, measurement biases, fluid phase changes). This allows the complex problem of accuracy improvement to be divided into manageable components while maintaining overall system precision.
Solution Approach 2:
A discrepancy event detector acts as an intermediary component between empirical measurements and flow rate estimation. This mediator compares simulated versus actual measurements, identifies discrepancy events, and triggers appropriate corrections, thereby improving accuracy without requiring direct modification of the measurement process itself.
2Reliability
If discrepancy detection and correction systems are implemented, then flow rate estimation accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The system dynamically adjusts its operation based on detected discrepancy events. Rather than continuously applying complex corrections, the system activates specific correction mechanisms only when discrepancy events are detected, optimizing the balance between reliability improvement and system complexity.
Solution Approach 2:
The system changes operational parameters (such as measurement values or model parameters) based on detected discrepancy events. For example, when measurement bias is detected, the system adjusts the empirical measurements accordingly, thereby improving reliability through parameter modification rather than system structural changes.
3Adaptability or versatility
If transient pipeline models are used, then the model can capture dynamic behavior, but consistent measurements are not expected leading to increased measurement discrepancies
Solution Approach 1:
The system converts the inherent measurement inconsistencies of transient models into beneficial information. By detecting these discrepancies as meaningful signals rather than errors, the system identifies transient events (such as fluid phase changes or dynamic flow conditions) and applies appropriate corrections, thereby improving accuracy while maintaining model versatility.
Data Source
AI summary
Techniques for detecting and correcting for discrepancy events in a fluid pipeline are presented. The techniques can include obtaining a plurality of empirical temperature and pressure measurements at a plurality of locations within the pipeline; simulating, using a pipeline model, a plurality of simulated temperature and pressure measurements for the plurality of locations within the pipeline; detecting, by a discrepancy event detector, at least one discrepancy event representing a discrepancy between the empirical temperature and pressure measurements and the simulated temperature and pressure measurements; outputting to a user an indication that the at least one discrepancy event has been detected; accounting for the at least one discrepancy; determining, after the accounting and using an estimator applied to the pipeline model, a corrected branch flow rate for the pipeline; and outputting the corrected branch flow rate for the pipeline to the user.