| 000 | 03216nlm1a2200469 4500 | ||
|---|---|---|---|
| 001 | 668694 | ||
| 005 | 20231030042157.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\39931 | ||
| 035 | _aRU\TPU\network\39881 | ||
| 090 | _a668694 | ||
| 100 | _a20230118a2022 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aDE | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aAdvance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm _fM. A. Ayman, A. A. Al-qaness Mohammed, A. E. Ahmed [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 330 | _aOil production forecasting is an important task to manage petroleum reservoirs operations. In this study, a developed time series forecasting model is proposed for oil production using a new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tJournal of Petroleum Exploration and Production | ||
| 463 |
_tVol. 12, iss. 2 _v[P. 383–395] _d2022 |
||
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aANFIS | |
| 610 | 1 | _aslime mould algorithm | |
| 610 | 1 | _aoilfield | |
| 610 | 1 | _atime series forecasting | |
| 610 | 1 | _aoil production | |
| 610 | 1 | _aдобыча нефти | |
| 701 | 1 |
_aAyman _bM. A. _gMutahar AlRassas |
|
| 701 | 1 |
_aMohammed _bA. A. Al-qaness |
|
| 701 | 1 |
_aAhmed _bA. E. _gEwees |
|
| 701 | 1 |
_aShaoran _bR. _gRen |
|
| 701 | 1 |
_aRenyuan _bS. _gSun |
|
| 701 | 0 | _aLin Pan | |
| 701 | 1 |
_aMokhamed Elsaed (Mohamed Abd Elaziz) _bA. M. _cSpecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University _f1987- _gAkhmed Mokhamed _2stltpush _3(RuTPU)RU\TPU\pers\46943 |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20230118 _gPSBO |
|
| 856 | 4 | _uhttps://doi.org/10.1007/s13202-021-01405-w | |
| 942 | _cCF | ||