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<p>The rate of oil production from wells is a key element influencing the economy of oil-producing nations and corporations. Accurate estimation of choke oil flow rate from surface-related parameters are critical to production optimization of oil fields. This is the first study that addresses the challenge of accurately predicting oil production rates by utilizing various advanced machine learning methods including Random Forest, convolutional neural network, support vector machine, multilayer perceptron artificial neural network and ridge regression methods. The goal is to construct a robust framework to proficiently calculate the oil production rates of wells while taking into account tubing head pressure (THP), choke size, BS&amp;W, gas oil ratio (GOR), and oil API. To ensure data integrity, the Leverage technique is employed to identify potential outlier data within the dataset, which consists of 195 data points. Furthermore, a sensitivity analysis is conducted to quantify the relative effect of each input parameter on the oil rates. The k-fold cross-validation technique is utilized in every algorithm to mitigate the overfitting problem during the training of models. The findings indicate that Random Forest outperforms the other algorithms, reaching a coefficient of determination (R<sup>2</sup>) of 0.96127 during evaluation, with the lowest error metrics. In contrast, ridge regression demonstrated a lower R<sup>2</sup> of 0.8666 in the evaluation phase. It is also proposed that choke size, the primary factor, THP, and API typically enhance the level of oil rate, whereas BS&amp;W and GOR decrease the oil production rate. The novelty of this study lies in the comprehensive comparison of machine learning methods applied to real-world oil production data, with a particular focus on the impact of key well parameters on production efficiency. This approach represents a significant advancement over previous efforts in the literature, offering more accurate and reliable predictions for oil production forecasting.</p>

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Precise prediction of choke oil rate in critical flow condition via surface data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月10日

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  • Qing Wang,
  • Muntadher Abed Hussein,
  • Bhavesh Kanabar,
  • Anupam Yadav,
  • Asha Rajiv,
  • Aman Shankhyan,
  • Sachin Jaidka,
  • Mehul Manu,
  • Issa Mohammed Kadhim,
  • Zainab Jamal Hamoodah,
  • Fadhil Faez,
  • Mohammad Mahtab Alam,
  • Hojjat Abbasi

摘要

The rate of oil production from wells is a key element influencing the economy of oil-producing nations and corporations. Accurate estimation of choke oil flow rate from surface-related parameters are critical to production optimization of oil fields. This is the first study that addresses the challenge of accurately predicting oil production rates by utilizing various advanced machine learning methods including Random Forest, convolutional neural network, support vector machine, multilayer perceptron artificial neural network and ridge regression methods. The goal is to construct a robust framework to proficiently calculate the oil production rates of wells while taking into account tubing head pressure (THP), choke size, BS&W, gas oil ratio (GOR), and oil API. To ensure data integrity, the Leverage technique is employed to identify potential outlier data within the dataset, which consists of 195 data points. Furthermore, a sensitivity analysis is conducted to quantify the relative effect of each input parameter on the oil rates. The k-fold cross-validation technique is utilized in every algorithm to mitigate the overfitting problem during the training of models. The findings indicate that Random Forest outperforms the other algorithms, reaching a coefficient of determination (R2) of 0.96127 during evaluation, with the lowest error metrics. In contrast, ridge regression demonstrated a lower R2 of 0.8666 in the evaluation phase. It is also proposed that choke size, the primary factor, THP, and API typically enhance the level of oil rate, whereas BS&W and GOR decrease the oil production rate. The novelty of this study lies in the comprehensive comparison of machine learning methods applied to real-world oil production data, with a particular focus on the impact of key well parameters on production efficiency. This approach represents a significant advancement over previous efforts in the literature, offering more accurate and reliable predictions for oil production forecasting.