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Related Concept Videos

Measurement of Fluid Pressure01:16

Measurement of Fluid Pressure

313
Fluid pressure is commonly measured using devices called manometers, which rely on liquid columns to indicate pressure differences. The height of a liquid column in a manometer reflects the pressure exerted by the fluid, providing a simple yet effective means of measurement. Different types of manometers serve specific purposes based on their configurations and the type of fluids involved.
A basic form of manometer is the piezometer, a vertical tube open at the top and filled with the same...
313
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

415
In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
415
Fluid Pressure01:14

Fluid Pressure

806
In mechanical engineering, fluid pressure plays a critical role in designing systems that utilize liquid flow, such as hydraulic systems, pumps, and valves. When designing these systems, engineers must ensure they can withstand the forces created by fluid pressure to avoid damage or failure.
According to Pascal's law, a fluid at rest will generate equal pressure in all directions. This pressure is measured as a force per unit area, and its magnitude depends on the fluid's specific...
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Related Experiment Video

Updated: Sep 17, 2025

Mechanical Expansion of Steel Tubing as a Solution to Leaky Wellbores
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Machine learning enhanced formation pressure prediction using integrated well logging and mud logging.

Jiwen Liang1, Ming Luo1, Wentuo Li1

  • 1Hainan Branch of CNOOC (China) Co., Ltd, Haikou, 570100, Hainan, China.

Scientific Reports
|July 3, 2025
PubMed
Summary

Accurately predicting high-pressure formation pressure is crucial in petroleum engineering. A new integrated model using well logging and mud logging data, particularly the IWM-GABP machine learning approach, significantly improves prediction accuracy, reducing drilling risks.

Keywords:
Abnormally high-pressureAccurate predictionIntegrated logging dataMachine learningPore pressure

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Area of Science:

  • Petroleum Engineering
  • Geoscience
  • Machine Learning Applications

Background:

  • Accurate prediction of abnormally high-pressure formation pressure is a critical challenge in petroleum engineering.
  • Inaccurate predictions lead to safety incidents like leakage and blowouts due to a narrow drilling safety density window.
  • Improving pore pressure prediction accuracy is essential for safe and efficient oil and gas exploration.

Purpose of the Study:

  • To develop a more accurate model for predicting formation pore pressure.
  • To investigate the correlation between various well logging and mud logging parameters and pore pressure.
  • To establish a machine learning model integrating well logging and mud logging data for enhanced prediction accuracy.

Main Methods:

  • Spearman correlation coefficient analysis to determine relationships between pore pressure and logging parameters.
  • Development of an integrated well logging and mud logging data (IWM) machine learning model.
  • Comparison of traditional models with the IWM model, specifically evaluating the IWM-GABP (backpropagation neural network with genetic algorithm) and IWM-RBFN (radial basis function network) models.

Main Results:

  • Pore pressure showed varying correlations with parameters like depth, weight on bit, and acoustic transit time.
  • The IWM-GABP model achieved the highest prediction accuracy, exceeding 96%.
  • The IWM model demonstrated an average improvement of 8.32% in prediction accuracy compared to traditional models, significantly reducing prediction error.

Conclusions:

  • The integrated well logging and mud logging data machine learning model, particularly IWM-GABP, offers superior accuracy for formation pressure prediction.
  • Utilizing the IWM-GABP or IWM-based backpropagation neural network model is recommended over the IWM-RBFN model for formation pressure prediction.
  • This research provides a robust method to enhance formation pressure prediction accuracy, supporting efficient on-site oilfield development.