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• W3138985364 abstract "Abstract In the past few years, over hundreds of wells were drilled in Gulf of Thailand, had faced with the depletion and lost circulation issues resulted from a lack of pressure data. A prior research of reservoir depletion pressure (Fangming, 2009) in oil field, China was obtained from multivariate statistic and regression by using density and neutron porosity log curves in logging-while-drilling data. However, the relative errors are 7.5% from the actual formation pressure. Thus, there are several latent variables in the model like drilling parameters (Rehm, 1971) which part of formation pressure. From 2018 initiative model in Satun-Funan, the classification model was obtained by using mud gas, porosity, water saturation, net sand thickness, net-hydrocarbon-pore thickness and neutron-density separation. However, the limitation is drilling parameters could not account by classifier, and accurate only original pressure category. So, this study has expanded scope to include other reservoir properties and drilling parameters then applied with machine learning on offset well dataset by using three regressors such quantile, ridge and XGBoost regressors. The pore pressure estimation model aims to improve efficiency for making decision in execution phase, increasing confidence in perforation strategy. The model parameters, pay thickness, porosity, water saturation, original pressure from local pressure profile and total gas show are accounted into this model. As of regressor assumption, some facts are conducted to logarithm and perform 2nd polynomial feature for model flexibility. There are three steps for building model such as data manipulation, analysis and deployment. Two purposes of pressure prediction impact algorithm selection, for operational phase, quantile regressor is implemented to provide conservative prediction while Ridge or XGBoost regressors are alternatives for perforation strategy, provide mid case result of pressure prediction. Overall model performance was measured using root mean square error (RMSE) on train & test dataset which show approximately 1.2 and 1.5 ppg range of accuracy respectively from total 12 drilling projects in Pattani basin. Overall model fitting is within reasonable range of generalization capacity to apply with unknown data point (test set). The future model will continue to improve accuracy and manage imbalanced dataset between original pressure and depleted sands." @default.
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• W3138985364 date "2021-03-16" @default.
• W3138985364 modified "2023-10-11" @default.
• W3138985364 title "Pore Pressure Estimation by Using Machine Learning Model" @default.
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• W3138985364 doi "https://doi.org/10.2523/iptc-21490-ms" @default.
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