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035			_a(RuTPU)RU\TPU\network\39459
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090			_a668235
100			_a20220704a2022 k y0engy50 ba
101	0		_aeng
102			_aNL
135			_adrcn ---uucaa
181		0	_ai
182		0	_ab
200	1		_aRobust computational approach to determine the safe mud weight window using well-log data from a large gas reservoir _fS. Beheshtian, M. Rajabi, Sh. Davoodi [et al.]
203			_aText _celectronic
300			_aTitle screen
330			_aA key parameter when drilling for gas and oil is to determine the safe mud weight window (SMWW) to ensure wellbore stability as part of a quantitative risk assessment (QRA). This study determines SMWW by predicting acceptable upper and lower limits of the bottom hole pressure window during over-balance drilling method. A novel machine learning method is developed to predict SMWW from ten well-log input variables subject to feature selection. 3389 data records from three South Pars gas field (Iran) wells include data from: uncorrected spectral gamma ray; potassium; thorium; uranium; photoelectric absorption factor; neutron porosity; bulk formation density; corrected gamma ray adjusted for uranium content; shear-wave velocity and compressional-wave velocity. Combinations of these well logs are tuned to provide predictions of the SMWW, measured in terms of subsurface pore and fracture pressures, using machine learning (ML) algorithms hybridized with optimizers. The ML algorithms assessed are multiple layer extreme learning machine (MELM) and least squares support vector machine (LSSVM), hybridized with genetic (GA) and particle swarm (PSO) optimizers. This new algorithm (MELM) incorporates special features that improve its prediction performance, speeds up its training, inhibits overfitting and involves less optimization in the model's construction. By combining MELM with PSO, its optimum control parameters are rapidly determined. The results reveal that the MELM-PSO combination provides the highest SMWW prediction accuracy of four models evaluated. For the testing subset MELM-PSO achieves high prediction performance of pore pressure (RMSE = 12.76 psi; R2 = 0.9948) and fracture pressure (RMSE = 15.71 psi; R2 = 0.9967). Furthermore, the model demonstrates that once trained with data from a few wells, it can be successfully applied to predict unseen data in other South Pars gas field wells. The findings of this study can provide a better understanding of how ML methods can be applied to accurately predict SMWW.
333			_aРежим доступа: по договору с организацией-держателем ресурса
461			_tMarine and Petroleum Geology
463			_tVol. 142 _v[105772, 25 p.] _d2022
610	1		_aэлектронный ресурс
610	1		_aтруды учёных ТПУ
610	1		_asafe mud weight window
610	1		_amultiple extreme learning machine
610	1		_ahybrid machine learning algorithms
610	1		_aoptimized well-log feature selection
701		1	_aBeheshtian _bS. _gSaeed
701		1	_aRajabi _bM. _gMeysam
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	_aWood _bD. A. _gDavid
701		1	_aGhorbani _bH. _gHamzeh
701		1	_aMohamadian _bN. _gNima
701		1	_aAhmadi _bA. M. _gAlvar Mehdi
701		1	_aAhmadi _bA. M. _gAlvar Mehdi
701		1	_aBand _bSh. S. _gShahab
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 _c20220704 _gRCR
856	4		_uhttps://doi.org/10.1016/j.marpetgeo.2022.105772
942			_cCF