Machine Learning Analysis of Chemical Inhibitors Under High Salinity
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Solution Overview
Problem
Natural gas production operations suffer from insufficient quality of chemical inhibitors due to higher than expected salinity of fluids produced from gas reservoirs, leading to hydrate formation risks and operational inefficiencies.
Innovation Solution
A dynamic digital analysis system utilizing supervised machine learning integrates multiphase flow models and thermodynamic analysis to optimize chemical inhibitor quality and quantity in real-time, incorporating data from onshore and offshore operations, and employs machine learning models to predict and mitigate hydrate formation risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chemical inhibitors are used to prevent hydrate formation in natural gas production, then hydrate formation risks are reduced, but the quality of chemical inhibitors becomes insufficient due to higher than expected salinity of produced fluids
Solution Approach 1:
The system dynamically adjusts inhibitor concentration and composition parameters based on real-time salinity measurements and machine learning predictions. The ML model continuously optimizes inhibitor parameters (concentration, type, dosage) to maintain effectiveness despite varying salinity conditions, resolving the quality insufficiency problem while preventing hydrate formation.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where salinity measurements, inhibitor performance data, and hydrate formation indicators are continuously monitored and fed back to the ML model. The model then adjusts inhibitor quality parameters in response, creating a self-correcting system that maintains inhibitor effectiveness despite salinity variations.
2Reliability
If higher concentrations of chemical inhibitors are used to compensate for quality issues, then hydrate formation risks are reduced, but operational inefficiencies increase due to increased chemical usage and cost
Solution Approach 1:
The ML model determines the precise minimum inhibitor concentration and composition needed to prevent hydrate formation under specific conditions, avoiding both insufficient dosing and excessive chemical usage. This partial action approach optimizes the balance between reliability and operational efficiency by applying only the necessary amount of inhibitor.
Solution Approach 2:
The system dynamically changes inhibitor parameters (concentration, type, dosage timing) based on real-time conditions and ML predictions, allowing operational efficiency to be maintained while ensuring adequate hydrate prevention. The model adjusts parameters to achieve effectiveness without unnecessary chemical overhead.
3Device complexity
If traditional monitoring methods are used for chemical inhibitor quality, then system complexity is low, but the ability to detect and respond to quality issues in real-time is insufficient
Solution Approach 1:
The system replaces traditional mechanical/chemical monitoring methods with machine learning-based digital monitoring. The ML model processes multiple data streams (salinity sensors, flow meters, temperature/pressure sensors) to detect inhibitor quality issues with high precision, substituting complex physical monitoring equipment with intelligent software analysis that achieves superior detection accuracy.
Solution Approach 2:
The ML-based monitoring system performs multiple functions simultaneously: it monitors salinity, predicts inhibitor quality, detects hydrate formation risks, and provides control recommendations. This multi-functional approach achieves high measurement precision without proportionally increasing device complexity, as the same ML infrastructure serves multiple monitoring purposes.
Data Source
AI summary
A computer implemented method that enables dynamic digital analysis of chemical inhibitors utilizing machine learning is described. The method includes determining a concentration output using a machine learning model trained using data associated with thermodynamic chemical inhibitors; determining temperatures associated with a chemical inhibitor regeneration cycle using a machine learning model trained using temporal data; determining a liquid inventory using a machine learning model trained using data associated with flow rates; generating a model of a chemical inhibitor regeneration cycle based on the concentration output, the temperatures, and the liquid inventory; and executing the model by inputting real-time operating conditions associated with the chemical inhibitor regeneration cycle, wherein the model outputs chemical inhibitor concentrations associated with a production system.