Predictive Model Training for Floating Hydrocarbon Plant Downtime
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Solution Overview
Problem
Floating hydrocarbon production plants face significant unplanned downtime, leading to substantial revenue and cash flow losses due to inefficiencies in production performance monitoring, necessitating a more reliable method for predicting production performance parameters.
Innovation Solution
A computer-implemented method utilizing sensors to obtain plant data, training predictive models using data from similar plants, and providing performance parameter values based on these models to optimize production efficiency and minimize downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional production performance monitoring methods are used, then device complexity is reduced, but reliability of production performance prediction deteriorates leading to unplanned downtime
Solution Approach 1:
The patent combines data from multiple similar floating hydrocarbon production plants to train a single predictive model. This merging of data sources improves prediction reliability by providing more comprehensive training data, while the model itself remains a unified system rather than requiring separate complex systems for each plant.
Solution Approach 2:
The patent implements preliminary training of predictive models using historical plant data before actual production performance prediction is needed. This advance preparation ensures the model is ready and reliable when needed, reducing unplanned downtime by having predictions available beforehand rather than waiting for issues to occur.
2Measurement precision
If more data from multiple plants is used for training, then measurement precision of performance parameters improves, but loss of time for data collection and processing increases
Solution Approach 1:
The patent performs data collection and model training in advance, before actual production performance prediction is needed. By preparing the predictive model beforehand using data from multiple plants, the system eliminates the time-consuming process of collecting and processing data when predictions are urgently needed, while still achieving high measurement precision through comprehensive training data.
Data Source
AI summary
A computer-implemented method for providing a performance parameter value indicative of a production performance of a first floating hydrocarbon production plant. The first plant includes hydrocarbon processing equipment and a sensor for measuring a value of a process parameter of the hydrocarbon processing equipment. The method includes obtaining first plant data from the first plant, the data including data generated by the sensor, obtaining a trained predictive model for predicting or classifying the performance parameter value, and providing, based on the trained predictive model and the first plant data, the performance parameter value for the first plant. Obtaining the trained predictive model includes obtaining plant training data from a second floating hydrocarbon production plant, the data including data generated by a sensor for measuring a process parameter value of hydrocarbon processing equipment of the second plant, providing a predictive model, and training the predictive model using the plant training data.