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Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling:Extra tree compared with feed forward neural network model

引用

Petroleum 2022年第2期8卷 227-236页

作者： Emmanuel E.Okoro Tamunotonjo Obomanu Samuel E.Sanni David I.Olatunji Paul Igbinedion Petroleum Engineering Department Covenant UniversityOtaNigeria Petroleum Engineering Department Federal Polytechnic of Oil and GasBonnyNigeria Chemical Engineering Department Covenant UniversityOtaNigeria

This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)*** six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while *** the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill *** Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field *** modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new *** gathered data encompasses a variety of well data,general information/data,depths,hole size,and *** developed model was compared with data obtained from two new fields based on its capability,stability and *** result and model’s performance from the error analysis revealed that the two proposed Extra Tree and feed forward models replicate the bottom hole pressure data with R2 greater than *** high values of R^(2) for the two models suggest the relative reliability of the modelling *** magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for feed forward model respectively;the least errors were recorded for the Extra Tree ***,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the feed forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the feed forward *** has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on t

关键词： Artificial intelligence Bottom hole pressure Extra tree Predictive model Oil and gas feed forward algorithms

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