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Energy Geoscience 2024年第2期5卷 225-231页

作者： goutami das Saumen Maiti Machine Learning and Geocomputing Laboratory Department of Applied GeophysicsIIT(ISM)Dhanbad826004JharkhandIndia

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field *** prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore ***,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex *** overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical *** empirical method for PP prediction is comprised of data pre-processing and model establishment ***'s method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition ***-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework *** normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone *** statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model *** dataset is prepared and divided into two parts:Training and *** PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression *** Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

关键词： Well log Pore pressure Machine learning(ML) IODP Hikurangi Tuaheni Zone IODP Expedition 372

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