| 000 | 03378nlm1a2200481 4500 | ||
|---|---|---|---|
| 001 | 665667 | ||
| 005 | 20231030042011.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\36870 | ||
| 035 | _aRU\TPU\network\34931 | ||
| 090 | _a665667 | ||
| 100 | _a20211028a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aDE | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aNovel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data _fM. Rajabi, S. Beheshtian, Sh. Davoodi [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 330 | _aOne of the challenges in reservoir management is determining the fracture density (FVDC) in reservoir rock. Given the high cost of coring operations and image logs, the ability to predict FVDC from various petrophysical input variables using a supervised learning basis calibrated to the standard well is extremely useful. In this study, a novel machine learning approach is developed to predict FVDC from 12-input variable well-log based on feature selection. To predict the FVDC, combination of two networks of multiple extreme learning machines (MELM) and multi-layer perceptron (MLP) hybrid algorithm with a combination of genetic algorithm (GA) and particle swarm optimizer (PSO) has been used. We use a novel MELM-PSO/GA combination that has never been used before, and the best comparison result between MELM-PSO-related models with performance test data is RMSE = 0.0047 1/m; R2 = 0.9931. According to the performance accuracy analysis, the models are MLP-PSO < MLP-GA < MELM-GA < MELM-PSO. This method can be used in other fields, but it must be recalibrated with at least one well. Furthermore, the developed method provides insights for the use of machine learning to reduce errors and avoid data overfitting in order to create the best possible prediction performance for FVDC prediction. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tJournal of Petroleum Exploration and Production | ||
| 463 |
_tVol. 11, iss. 12 _v[P. 4375-4397] _d2021 |
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| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _afracture density | |
| 610 | 1 | _amulti-hidden layer extreme learning machine | |
| 610 | 1 | _ahybrid machine learning algorithms | |
| 610 | 1 | _amulti-layer perceptron | |
| 610 | 1 | _aплотность | |
| 610 | 1 | _aтрещины | |
| 610 | 1 | _aгибридное обучение | |
| 701 | 1 |
_aRajabi _bM. _gMeysam |
|
| 701 | 1 |
_aBeheshtian _bS. _gSaeed |
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| 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 | 1 |
_aGhorbani _bH. _gHamzeh |
|
| 701 | 1 |
_aMohamadian _bN. _gNima |
|
| 701 | 1 |
_aRadwan _bA. E. _gAhmed |
|
| 701 | 1 |
_aAhmadi _bA. M. _gAlvar Mehdi |
|
| 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 _c20211028 _gPSBO |
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| 856 | 4 | _uhttps://doi.org/10.1007/s13202-021-01321-z | |
| 942 | _cCF | ||