Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of the ROP based on GA-LightGBM and drilling data
Constructing oil and gas wells is one of the most expensive activities in the petroleum sector, and increasing drilling speed is crucial for reducing costs, minimizing non-productive time (NPT), and improving efficiency. To address this challenge, a data-driven predictive model for mechanical drilling speed was developed using an artificial intelligence approach. Several machine learning algorithms – including decision trees, support vector regression, k-nearest neighbors, neural networks, XGBoost, and LightGBM – were selected to thoroughly investigate the nonlinear relationship between drilling data and the rate of penetration (ROP). These models were trained, evaluated, and compared, with results indicating that the LightGBM algorithm provided the most accurate ROP predictions. Building on this, the LightGBM model was further refined using cross-validation techniques and a genetic algorithm (GA) to optimize its hyperparameters, resulting in even greater accuracy. The research findings demonstrate that the GA-LightGBM algorithm achieves high precision in ROP prediction on the test set (MAE = 2.39, MSE = 10.53, R2 = 0.93). This model effectively predicts mechanical drilling speed in various well sections, thereby aiding in optimizing the drilling process, improving production efficiency, and reducing costs.
Utilize GA to fine-tune LightGBM hyperparameters and construct the GA_LightGBM model for ROP prediction.
Utilizing artificial intelligence tools for predicting ROP.
Modeling ROP prediction uncertainty using specific oilfield drilling parameters.
Prediction of the ROP based on GA-LightGBM and drilling data
Constructing oil and gas wells is one of the most expensive activities in the petroleum sector, and increasing drilling speed is crucial for reducing costs, minimizing non-productive time (NPT), and improving efficiency. To address this challenge, a data-driven predictive model for mechanical drilling speed was developed using an artificial intelligence approach. Several machine learning algorithms – including decision trees, support vector regression, k-nearest neighbors, neural networks, XGBoost, and LightGBM – were selected to thoroughly investigate the nonlinear relationship between drilling data and the rate of penetration (ROP). These models were trained, evaluated, and compared, with results indicating that the LightGBM algorithm provided the most accurate ROP predictions. Building on this, the LightGBM model was further refined using cross-validation techniques and a genetic algorithm (GA) to optimize its hyperparameters, resulting in even greater accuracy. The research findings demonstrate that the GA-LightGBM algorithm achieves high precision in ROP prediction on the test set (MAE = 2.39, MSE = 10.53, R2 = 0.93). This model effectively predicts mechanical drilling speed in various well sections, thereby aiding in optimizing the drilling process, improving production efficiency, and reducing costs.
Utilize GA to fine-tune LightGBM hyperparameters and construct the GA_LightGBM model for ROP prediction.
Utilizing artificial intelligence tools for predicting ROP.
Modeling ROP prediction uncertainty using specific oilfield drilling parameters.
Prediction of the ROP based on GA-LightGBM and drilling data
Wang, Shuai Shuai (Autor:in) / Yan, Jun (Autor:in) / Geng, Hao (Autor:in)
Geosystem Engineering ; 28 ; 12-30
02.01.2025
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM
| DOAJ | 2023
A Hybrid Prediction Framework for Water Quality with Integrated W-ARIMA-GRU and LightGBM Methods
| DOAJ | 2022