A machine learning-driven stochastic simulation of underground sulfide distribution with multiple constraints
The increase of sulfide (S2−) during the water flooding process has been regarded as an essential and potential risk for oilfield development and safety. Kriging and stochastic simulations are common methods for assessing the element distribution. However, these traditional simulation methods are no...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2021-07-01
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| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2020-0274 |