Volume 42 Issue 3
Jun.  2025
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GUO Liang, XU Hang, LIU Kaiyong, et al.Method of predicting drilling fluid rheology based on generative adversarial networks in digital twin environment[J]. Drilling Fluid & Completion Fluid，2025, 42（3）：359-367 doi: 10.12358/j.issn.1001-5620.2025.03.012
Citation: GUO Liang, XU Hang, LIU Kaiyong, et al.Method of predicting drilling fluid rheology based on generative adversarial networks in digital twin environment[J]. Drilling Fluid & Completion Fluid，2025, 42（3）：359-367 doi: 10.12358/j.issn.1001-5620.2025.03.012
shu

Method of Predicting Drilling Fluid Rheology Based on Generative Adversarial Networks in Digital Twin Environment

doi: 10.12358/j.issn.1001-5620.2025.03.012
  • 1.

    School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, Sichuan 610000

  • 2.

    Sichuan Shared Platform for Oil and Gas Equipment Technology, Chengdu, Sichuan 610000

  • 3.

    CNPC Great Wall Drilling Engn Co Ltd, Drilling Fluid Co, Panjin, Liaoning124000

  • Received Date: 2024-12-06
  • Rev Recd Date: 2025-02-25
  • Publish Date: 2025-06-12
    Abstract
  • A method of predicting drilling fluid rheology based on genitive adversarial network in digital twin environment has been developed to deal with problems in laboratory measurement of drilling fluid rheology manually, such as low efficiency, high cost and poor stability etc. First, a twin model for drilling fluid formulation and measurement system is constructed in accordance with digital twin five-dimensional model. Information collectors such as sensors in the physical measurement system can collect the live measured data of drilling fluid properties, and the composition of the drilling fluid and the measured drilling fluid properties are integrated and sent to a virtual space to construct a database for drilling fluid property prediction. Second, using the improved generative adversarial network algorithm, a drilling fluid rheology prediction model is constructed. Historical twin data of the drilling fluid are then extracted from the database and are used as a dataset to train the model, and a best-fitting model is thus obtained. The prediction ability of the model is verified through the prediction experiment of drilling fluid rheology. Use of the model shows that the correlation coefficient R between the predicted values and the true values exceeds 0.96, and the mean absolute percentage error (AAPE) is not higher than 4.1%, indicating that the model has higher prediction accuracy, and is able to accomplish the predciiton of drilling fluid rheology.

     

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