AI Oil Well Model Predicts Wax Deposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for addressing wax deposition and hydrate buildup in oil wells are costly and inadequate, leading to decreased well flow rates and potential blockages, especially in hard-to-reach areas like offshore wells.
Innovation Solution
A system and method utilizing artificial intelligence and machine learning to model and predict wax/hydrate deposition in oil wells, building scalable models from historical data to estimate deposition conditions, thereby optimizing maintenance planning and reducing shutdowns and maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repeated periodic wax removal methods (mechanical removal, heating, chemical inhibition) are used, then wax deposition is addressed, but maintenance costs increase and operational continuity is disrupted
Solution Approach 1:
The system performs preliminary action by continuously monitoring well parameters (temperature, pressure, flow rate) and predicting wax deposition trends before blockage occurs. This allows scheduling maintenance at optimal intervals rather than reacting to failures, reducing both shutdown time and maintaining reliability.
Solution Approach 2:
The system implements feedback through continuous monitoring of well parameters and comparing predicted deposition rates against thresholds. This closed-loop approach adjusts maintenance scheduling dynamically, preventing both premature maintenance (wasting time) and delayed maintenance (losing reliability).
2Reliability
If repeated periodic wax removal methods are used, then wax deposition is addressed, but operational costs increase
Solution Approach 1:
By predicting wax deposition trends in advance, the system schedules maintenance only when necessary, avoiding unnecessary heating operations, chemical injections, and mechanical interventions. This reduces energy consumption and operational costs while maintaining flow rate reliability.
Solution Approach 2:
The system changes the approach from fixed-interval maintenance to condition-based maintenance by continuously monitoring parameters like temperature differential, pressure drop, and flow rate variations. This allows optimizing maintenance timing to minimize costs while ensuring reliability.
Data Source
AI summary
A method and system for estimating wax or hydrate deposits is desirable for the oil industry and important for assuring flow conditions and production, avoiding downtime, and reducing or preventing costly interventions. The method and system disclosed herein use artificial intelligence and machine learning techniques combined with oil well historical operational sensor data and historical operational event records (such as diesel hot flush, slick line, coil tubing, etc.) to build an oil well model. The method and system enable oil well practitioners to test and validate the built model and deploy the model online to estimate and/or detect wax or hydrate deposition status. By using one or more such models in operating an oil well, users can monitor and/or detect the status of wax of hydrate deposits in an oil well and can optimize production, maintenance, and planning for oil wells.