首页|Universitas Andalas Researchers Update Current Data on Machine Learning [Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data]
Universitas Andalas Researchers Update Current Data on Machine Learning [Sonic Log Prediction Based on Extreme Gradient Boosting (XGBoost) Machine Learning Algorithm by Using Well Log Data]
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Investigators discuss new findings in artificial intelligence. According to news originating from the Universitas Andalas by NewsRx editors, the research stated, “Sonic log is an important aspect that provides a detailed description of the subsurface properties associated with oil and gas reservoirs.” The news correspondents obtained a quote from the research from Universitas Andalas: “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%.”
Universitas AndalasAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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