Method and system for predicting and evaluating working condition of deep sea drilling fluid lifting system

By constructing a physical model-based database of operating point samples and training a machine learning proxy model, the problems of response lag and insufficient accuracy in the prediction of operating conditions for deep-sea drilling fluid lift systems were solved, enabling rapid and accurate operating condition prediction and frequency optimization, thereby improving the safety and efficiency of deep-sea drilling operations.

CN122021981APending Publication Date: 2026-05-12EXPLORATION TECH RES INST OF CHINESE ACADEMY OF GEOLOGICAL SCI
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Patent Information

Application Number
CN202511259025.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-04
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Existing technologies cannot quickly and accurately predict the operating conditions of deep-sea drilling fluid lift systems, especially when drilling fluid density, pump configuration, and power supply frequency change. They cannot meet the needs of real-time control and optimization, resulting in response lag, large computational load, and insufficient accuracy.

Method used

Using a machine learning proxy model, a physical model is constructed based on historical operating data of deep-sea drilling fluid lift systems to generate a database of operating point samples. By training the machine learning proxy model, the power supply frequency is predicted and optimized in real time, providing operating condition prediction results and frequency recommendations.

Benefits of technology

It enables a response to changes in drilling fluid density within milliseconds, providing real-time decision support, improving the accuracy and efficiency of working condition prediction, and enhancing the safety and economy of deep-sea drilling operations.

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Abstract

The invention discloses a working condition prediction and evaluation method and system for a deep sea drilling fluid lifting system, and relates to the technical field of ocean engineering equipment and working condition prediction and evaluation, and the method comprises the steps: generating a working condition point sample database based on a physical model; a machine learning agent model is constructed and trained; performing real-time working condition prediction and parameter optimization, and outputting a real-time working condition prediction result, a frequency recommendation result and / or a capability boundary analysis result; the working condition prediction result comprises corresponding predicted flow and predicted lift under various power supply frequencies and pump configuration combinations; the frequency recommendation result comprises an optimal power supply frequency which accords with a preset operation target set by a user and meets system security constraints; the capacity boundary analysis result comprises the lifting capacity of the deep sea drilling fluid lifting system under the limiting condition. According to the method, the operation condition points (flow and lift) of the double-pump series / parallel configuration under different power supply frequencies can be quickly and accurately predicted based on the drilling fluid density changing in real time, and the optimal power supply frequency can be intelligently recommended.
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