| 000 | 03835nlm1a2200481 4500 | ||
|---|---|---|---|
| 001 | 666867 | ||
| 005 | 20231030042051.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\38071 | ||
| 035 | _aRU\TPU\network\38002 | ||
| 090 | _a666867 | ||
| 100 | _a20220202a2022 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aA robust approach to pore pressure prediction applying petrophysical log data aided by machine learning techniques _fZhang Guodao, Sh. Davoodi, Shamshirband Shahab [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 320 | _a[References: P. 2245-2247] | ||
| 330 | _aDetermination of pore pressure (PP), a key reservoir parameter that is beneficial for evaluating geomechanical parameters of the reservoir, is so important in oil and gas fields development. Accurate estimation of PP is also essential for safe drilling of oil and gas wells since PP data are used as the input for safe mud window determination. In the present study, empirical equations along with machine learning methods, namely random forest algorithm, support vector regression (SVR) algorithm, artificial neural network (ANN) algorithm, and decision tree (DT) algorithm, are employed for PP prediction applying well log data. To this end, 2827 data records collected from three wells (Well A, Well B, and Well C) drilled in one of the Middle East oil fields are used. The dataset of Wells A and B is used for models' training, validating, and testing, while Well C dataset is applied for evaluating the models' generalizability in PP prediction in the field under study. To construct the predictive algorithms, 12 input variables are initially considered in the study. A feature selection analysis is conducted to find the most influential input variables set for developing PP predictive models. The results obtained suggest that the 9-input-variable set is the most efficient combination of inputs used in the ML models construction. Among all the four ML algorithms proposed, the DT algorithm presents the most accurate predictions for PP, delivering R2 and RMSE values of 0.9985 and 14.460 psi, respectively. Furthermore, the model generalization analysis results reveal that the 9-input-variable DT model developed can be used for PP prediction throughout the field of study since it presented an excellent accuracy performance in predicting PP when applied to Well C dataset. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tEnergy Reports | ||
| 463 |
_tVol. 8 _v[P. 2233-2247] _d2022 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _apore pressure | |
| 610 | 1 | _amachine learning algorithms | |
| 610 | 1 | _apetrophysical data | |
| 610 | 1 | _adecision tree algorithm | |
| 610 | 1 | _aалгоритмы | |
| 610 | 1 | _aмашинное обучение | |
| 610 | 1 | _aпетрофизические данные | |
| 610 | 1 | _aпоровое давление | |
| 701 | 0 | _aZhang Guodao | |
| 701 | 1 |
_aDavoodi _bSh. _cspecialist in the field of petroleum engineering _cResearch Engineer of Tomsk Polytechnic University _f1990- _gShadfar _2stltpush _3(RuTPU)RU\TPU\pers\46542 |
|
| 701 | 0 | _aShamshirband Shahab | |
| 701 | 0 | _aGhorbani Hamzeh | |
| 701 | 0 | _aMosavi Amir | |
| 701 | 0 | _aMoslehpour Massoud | |
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа природных ресурсов _bОтделение нефтегазового дела _h8084 _2stltpush _3(RuTPU)RU\TPU\col\23546 |
| 801 | 2 |
_aRU _b63413507 _c20220202 _gRCR |
|
| 856 | 4 | _uhttps://doi.org/10.1016/j.egyr.2022.01.012 | |
| 942 | _cCF | ||