Real-time measurement method of drilling fluid rheological parameters based on multi-objective inversion
•A real-time measurement method of drilling fluid rheological parameters is developed.•A surrogate model between the differential pressure and rheological parameters is set up.•An inversion model for rheological parameters with multi-objective JAYA algorithm is proposed.•The applicability and effect...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 223; S. 113706 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.12.2023
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| Schlagworte: | |
| ISSN: | 0263-2241 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | •A real-time measurement method of drilling fluid rheological parameters is developed.•A surrogate model between the differential pressure and rheological parameters is set up.•An inversion model for rheological parameters with multi-objective JAYA algorithm is proposed.•The applicability and effectiveness are verified in a real experimental device.
Real-time online fast and accurate measurement of drilling fluid performance parameters is very important in the petroleum extraction process. At present, there are few studies on the measurement of drilling fluid performance parameters, and the measurement of drilling fluid performance parameters on the well site mainly uses a rotational viscometer, which cannot achieve online real-time measurement. Therefore, this study developed a real-time online measurement system for drilling fluid rheological performance parameters based on the principle of pipeline differential pressure measurement, and realized online measurement of parameters such as yield stress, liquidity index, and consistency coefficient. In order to obtain accurate three rheological parameters, this study conducted a numerical simulation study of the drilling fluid measurement system based on the finite element method to realize the analysis of fluid pressure distribution in the pipeline. Secondly, the surrogate model between the differential pressure and rheological parameters, which is based on an extreme learning machine (ELM) optimized by particle swarm algorithm (PSO) is set up. Finally, based on the accuracy and robustness of the multi-objective function of inversion calculation, an intelligent inversion model for rheological parameters with a multi-objective JAYA algorithm is proposed to realize real-time online fast and accurate acquisition of drilling fluid rheological parameters. |
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| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2023.113706 |