Scholarlink

<p>Pore pressure (PP) information is essential for gas hydrate exploration, field development, and other geological modelling studies. The prediction of PP from borehole data is difficult due to the complex (non-linear) relationship between the input (e.g., well log; over-burden stress) and target variables (PP). An ensemble learning-based interpretable method (ELBIM) is proposed to model PP using multivariate borehole data of the IODP expedition 372 drill site U1517A of the Northern Hikurangi Margin, New Zealand. The predictive model is configured by taking input of seven petrophysical parameters (e.g., NMR porosity, caliper, gamma-ray, density, neutron porosity, temperature, and sonic transit time) with lags based on auto-correlation function (ACF) and partial autocorrelation function (PACF) analyses of well log and target PP using empirical techniques (Eaton’s and Porosity method). Comparative analysis showed that the ELBIM with extreme gradient boosting (XGB) regression outperformed gradient boosting machine (GB), random forest regression (RFR), and decision tree regression (DTR) based on root-mean-square errors (RMSEs) RMSE<sub>XGB</sub> ~0.0087, RMSE<sub>GB</sub> ~0.0090, RMSE<sub>RFR</sub>~0.0123 and RMSE <sub>DTR</sub> ~0.0165, and coefficients of determination (R<sup>2</sup>) of R<sup>2</sup><sub>XGB</sub> ~ 0.9954, R<sup>2</sup><sub>GB</sub> ~ 0.9950, R<sup>2</sup><sub>RFR</sub> ~ 0.9908 and R<sup>2</sup><sub>DTR</sub> ~ 0.9834 between the observation and the prediction of PP with 95% confidence interval (CI). The present analysis suggests that the ELBIM with XGB yields more precise forecasts of PP and exhibits improved detection of the bottom-simulating reflector (BSR) at the drill site. The proposed ELBIM with XGB has the potential to predict the PP from multivariate well logs and could be applied to other complex areas.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Ensemble learning-based interpretable method for pore pressure prediction using multivariate well logging data of IODP site U1517

  • 期刊论文

  • 发表日期：

    2025年1月25日

查看全文
  • Goutami Das,
  • Saumen Maiti

摘要

Pore pressure (PP) information is essential for gas hydrate exploration, field development, and other geological modelling studies. The prediction of PP from borehole data is difficult due to the complex (non-linear) relationship between the input (e.g., well log; over-burden stress) and target variables (PP). An ensemble learning-based interpretable method (ELBIM) is proposed to model PP using multivariate borehole data of the IODP expedition 372 drill site U1517A of the Northern Hikurangi Margin, New Zealand. The predictive model is configured by taking input of seven petrophysical parameters (e.g., NMR porosity, caliper, gamma-ray, density, neutron porosity, temperature, and sonic transit time) with lags based on auto-correlation function (ACF) and partial autocorrelation function (PACF) analyses of well log and target PP using empirical techniques (Eaton’s and Porosity method). Comparative analysis showed that the ELBIM with extreme gradient boosting (XGB) regression outperformed gradient boosting machine (GB), random forest regression (RFR), and decision tree regression (DTR) based on root-mean-square errors (RMSEs) RMSEXGB ~0.0087, RMSEGB ~0.0090, RMSERFR~0.0123 and RMSE DTR ~0.0165, and coefficients of determination (R2) of R2XGB ~ 0.9954, R2GB ~ 0.9950, R2RFR ~ 0.9908 and R2DTR ~ 0.9834 between the observation and the prediction of PP with 95% confidence interval (CI). The present analysis suggests that the ELBIM with XGB yields more precise forecasts of PP and exhibits improved detection of the bottom-simulating reflector (BSR) at the drill site. The proposed ELBIM with XGB has the potential to predict the PP from multivariate well logs and could be applied to other complex areas.