Wellbore Pump Load Prediction Using Neural Networks and Bayesian Tuning
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Solution Overview
Problem
Existing pump load prediction models for wellbore pumps are often inaccurate and not generated in real-time or near real-time, leading to inefficiencies in hydrocarbon production operations.
Innovation Solution
The implementation of a neural network framework that utilizes a physics-based model to predict pump load, combined with Bayesian Optimization to match predicted and measured pump loads, allowing for real-time or near real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation and model methods are used to predict pump load, then computational complexity is reduced, but prediction accuracy deteriorates and real-time capability is lost
Solution Approach 1:
The system performs preliminary actions by pre-processing historical pump load data and establishing the relationship between pump load and hydrocarbon production rate before real-time prediction is needed. The neural network is trained offline with historical data, creating a ready-to-use predictive model that can quickly process real-time measurements without requiring complex computations during actual prediction.
Solution Approach 2:
The patent replaces traditional mechanical simulation and modeling approaches with a neural network-based system. Instead of using complex physics-based simulations that require significant computational resources, the system uses machine learning algorithms that have learned patterns from historical data, enabling fast and accurate predictions without heavy computational machinery.
2Reliability
If traditional simulation models are used, then device complexity is reduced, but prediction reliability deteriorates
Solution Approach 1:
The neural network system performs self-service by automatically learning and adapting to the specific characteristics of each wellbore pump through training on historical data from that particular pump. The system self-calibrates by processing measured pump load data and automatically adjusting its internal parameters to optimize predictions, eliminating the need for manual model calibration and improving reliability without requiring complex external intervention.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted pump load values with actual measured pump load data. This feedback loop allows the neural network to learn from discrepancies between predictions and measurements, continuously improving its accuracy and reliability over time while maintaining a relatively simple system architecture.
Data Source
AI summary
The disclosed embodiments include pump systems and methods to improve pump load predictions of pumps. The method includes determining, in a neural network, a pump load of a wellbore pump based on a physics based model of the pump load of the wellbore pump. The method also includes obtaining a measured pump load of the wellbore pump. After initiation of a pump cycle of the wellbore pump, the method further includes predicting a pump load of the wellbore pump based on the physics based model, performing a Bayesian Optimization to reduce a difference between a predicted pump load and the measured pump load to less than a threshold value, and improving a prediction of the pump load based on the Bayesian Optimization.