使用机器学习来预测气体提升井下的油价的建模
Famin Ma1, Farag M A Altalbawy2, Pinank Patel3
1Shangluo University, Shangluo, 726000, Shannxi, China. mafamin_2007@163.com.
Scientific reports
|July 30, 2025
概括
这项研究开发了机器学习模型,以预测天然气提升井的石油生产率. 随机森林模型表现最好,识别了影响石油产量的关键因素,以优化运营.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习应用 机器学习应用
- 储水库工程 储水库工程
背景情况:
- 优化天然气提升系统的石油产量是复杂的,因为交互的操作和储参数.
- 准确预测石油生产速度对于高效的油井管理至关重要.
研究的目的:
- 开发和评估可靠的机器学习模型,用于估计天然气提升井的石油产量.
- 通过敏感性和可解释性分析,确定影响石油产量的关键参数.
主要方法:
- 利用了来自伊拉克油田的169个样本的数据集,包括沉积物含量,塞大小,压力和气体注射参数等特征.
- 训练和评估多个机器学习模型 (随机森林,CNN,SVR等). 使用五倍交叉验证和统计指标 (R2,MSE,AARE%).
- 应用敏感性分析和SHAP可解释性方法来确定对石油生产有影响的因素.
主要成果:
- 随机森林模型实现了最高的性能,测试R2为0.867,预测误差最低 (MSE:18502,AARE:8.76%).
- 灵敏度分析表明,基本沉积物和水含量,窒息物大小和上游压力显著影响石油生产.
- 其他模型表现出过度装配或装配不足的趋势,突出了随机森林方法的稳定性.
结论:
- 机器学习,特别是随机森林模型,提供了一个强大的工具,用于准确预测天然气提升井的石油产量.
- 了解特定参数的影响,如沉积物含量和窒息物大小,对于优化气体升降机操作至关重要.
- 该研究为提高石油回收效率和井性能提供了可操作的见解.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
08:38Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
Published on: January 16, 2018
10.6K
相关概念视频
01:07Residual Plots
5.0K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
5.0K
01:25Multiple Regression
3.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.2K
01:11Regression Analysis
6.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.0K
01:11Residuals and Least-Squares Property
7.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.8K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:18Steps in Outbreak Investigation
207
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
207