A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning; Journal of Petroleum Science and Engineering; Vol. 196
| Parent link: | Journal of Petroleum Science and Engineering Vol. 196.— 2021.— [107811, 21 p.] |
|---|---|
| Institution som forfatter: | |
| Andre forfattere: | , , , , , , , |
| Summary: | Title screen The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well design and operating procedures. However, certain rock formations and their fluid pressure and stress conditions are more prone to casing-collapse risks than others. The Gachsaran Formation in south west Iran, is one such formation, central to oil and gas resource exploration and development in the Zagros region and consisting of complex alternations of anhydrite, marl and salt. The casing-collapse incidents in this formation have resulted over decades in substantial lost production and remedial costs to mitigate the issues surrounding wells with failed casing string. High and vertically-varying horizontal stresses across the Gachsaran formation are the challenge to predict and overcome. This study investigates casing collapse in wellbores from an established petroleum geomechanics perspective to develop and compare two hybrid neural-network models, multilayer perceptron's tuned, respectively, with a genetic algorithm (MLP-GA) and a particle swarm algorithm (MLP-PSO). These machine-learning algorithms are configured to predict Poisson's ratio () and maximum horizontal stress () from available well-log input data. A large dataset from three wellbores drilled though the Gachsaran Formation in the Marun oil field, the second largest in Iran, is used to construct and validate the hybrid MLP models. In the first step, 22323 data records from a collapsed wellbore are divided into training (15626 records; 70%) and independent testing subsets (6697 records; 30%). In the next step, in order to test whether the suggested algorithms could be generalized, the data sets of the other two wells in the field were tested. Those tests confirmed that the algorithms achieved high prediction accuracy for Poisson's ratio and maximum horizontal stress in other wellbores. Режим доступа: по договору с организацией-держателем ресурса |
| Sprog: | engelsk |
| Udgivet: |
2021
|
| Fag: | |
| Online adgang: | https://doi.org/10.1016/j.petrol.2020.107811 |
| Format: | MixedMaterials Electronisk Book Chapter |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662893 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 662893 | ||
| 005 | 20250416092054.0 | ||
| 035 | |a (RuTPU)RU\TPU\network\34052 | ||
| 090 | |a 662893 | ||
| 100 | |a 20201123d2021 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 102 | |a NL | ||
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i | |
| 182 | 0 | |a b | |
| 200 | 1 | |a A geomechanical approach to casing collapse prediction in oil and gas wells aided by machine learning |f N. Mohamadian, H. Ghorbani, D. A. Wood [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 330 | |a The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well design and operating procedures. However, certain rock formations and their fluid pressure and stress conditions are more prone to casing-collapse risks than others. The Gachsaran Formation in south west Iran, is one such formation, central to oil and gas resource exploration and development in the Zagros region and consisting of complex alternations of anhydrite, marl and salt. The casing-collapse incidents in this formation have resulted over decades in substantial lost production and remedial costs to mitigate the issues surrounding wells with failed casing string. High and vertically-varying horizontal stresses across the Gachsaran formation are the challenge to predict and overcome. This study investigates casing collapse in wellbores from an established petroleum geomechanics perspective to develop and compare two hybrid neural-network models, multilayer perceptron's tuned, respectively, with a genetic algorithm (MLP-GA) and a particle swarm algorithm (MLP-PSO). These machine-learning algorithms are configured to predict Poisson's ratio () and maximum horizontal stress () from available well-log input data. A large dataset from three wellbores drilled though the Gachsaran Formation in the Marun oil field, the second largest in Iran, is used to construct and validate the hybrid MLP models. In the first step, 22323 data records from a collapsed wellbore are divided into training (15626 records; 70%) and independent testing subsets (6697 records; 30%). In the next step, in order to test whether the suggested algorithms could be generalized, the data sets of the other two wells in the field were tested. Those tests confirmed that the algorithms achieved high prediction accuracy for Poisson's ratio and maximum horizontal stress in other wellbores. | ||
| 333 | |a Режим доступа: по договору с организацией-держателем ресурса | ||
| 461 | |t Journal of Petroleum Science and Engineering | ||
| 463 | |t Vol. 196 |v [107811, 21 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a hybrid multi-layer perceptron's | |
| 610 | 1 | |a wellbore casing collapse | |
| 610 | 1 | |a maximum horizontal stress | |
| 610 | 1 | |a Poisson's ratio | |
| 610 | 1 | |a gachsaran formation | |
| 610 | 1 | |a geomechanical sub-surface model | |
| 610 | 1 | |a перцептроны | |
| 610 | 1 | |a обрушение пород | |
| 610 | 1 | |a горизонтальные деформации | |
| 610 | 1 | |a коэффициент Пуассона | |
| 701 | 1 | |a Mohamadian |b N. |g Nima | |
| 701 | 1 | |a Ghorbani |b H. |g Hamzeh | |
| 701 | 1 | |a Wood |b D. A. |g David | |
| 701 | 1 | |a Mehrad |b M. |g Mohammad | |
| 701 | 1 | |a Davoodi |b Sh. |c specialist in the field of petroleum engineering |c Research Engineer of Tomsk Polytechnic University |f 1990- |g Shadfar |3 (RuTPU)RU\TPU\pers\46542 |9 22200 | |
| 701 | 1 | |a Rashidi |b S. |g Sina | |
| 701 | 1 | |a Soleimanian |b A. |g Alireza | |
| 701 | 1 | |a Shahvand |b A. K. |g Amirafzal Kiani | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа природных ресурсов |b Отделение нефтегазового дела |3 (RuTPU)RU\TPU\col\23546 |9 28343 |
| 801 | 2 | |a RU |b 63413507 |c 20201123 |g RCR | |
| 850 | |a 63413507 | ||
| 856 | 4 | |u https://doi.org/10.1016/j.petrol.2020.107811 | |
| 942 | |c CF | ||