Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data

Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach...

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Vydané v:	BIO web of conferences Ročník 89; s. 9003
Hlavní autori:	Hakam, Abdul, Utama, Widya, Garini, Sherly Ardhya, Jabar, Omar Abdul, Insani, Alif Nurdien Fitrah, Rosandi, Yudi
Médium:	Journal Article Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	Les Ulis EDP Sciences 01.01.2024
Predmet:	Algorithms Availability Continuous improvement Data processing Gamma rays Geophysical exploration Learning algorithms Machine learning Oil reservoirs Optimization Porosity Prediction models Predictions Propagation velocity Tuning Wave propagation
ISSN:	2117-4458, 2273-1709, 2117-4458
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Abstract	Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%. The results also pointed out the need for methods other than listwise deletion to handle missing values as an alternative to improving model accuracy. This research highlighted the need for continuous improvement in data processing methods and algorithm optimization to advance the application of machine learning in geophysical exploration.
AbstractList	Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs. The problem that frequently occurs is the unavailability of sonic log data for various reasons needs to be given an effective solution. The alternative approach proposed in this research is sonic log prediction based on Extreme Gradient Boosting (XGBoost) machine learning algorithm, using available log data to build a reliable sonic log prediction model. In this research, the predicted DT log type is the Differential Time Shear Slowness (DTSM) log, which is the velocity of shear waves propagating in a formation. Log features used for training include gamma ray (GR), density (RHOB), porosity (NPHI), resistivity (RS and RD) logs with DTSM log as the prediction target. To optimise the performance and generalisation of the XGBoost algorithm in predicting log DTSM, hyperparameter tuning was applied using grid search technique to obtain optimal parameters for the prediction model. Based on the experimental results, this research found that hyperparameter tuning using grid search technique improved the accuracy of sonic log (DTSM) model prediction based on XGBoost algorithm, as proven by the decrease of RMSE and MAPE values to 19.699 and 7.713%. The results also pointed out the need for methods other than listwise deletion to handle missing values as an alternative to improving model accuracy. This research highlighted the need for continuous improvement in data processing methods and algorithm optimization to advance the application of machine learning in geophysical exploration.
Author	Garini, Sherly Ardhya Jabar, Omar Abdul Insani, Alif Nurdien Fitrah Rosandi, Yudi Hakam, Abdul Utama, Widya
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SubjectTerms	Algorithms Availability Continuous improvement Data processing Gamma rays Geophysical exploration Learning algorithms Machine learning Oil reservoirs Optimization Porosity Prediction models Predictions Propagation velocity Tuning Wave propagation
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Title	Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data
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