Summary of Generative Ai-driven Forecasting Of Oil Production, by Yash Gandhi et al.
Generative AI-driven forecasting of oil production
by Yash Gandhi, Kexin Zheng, Birendra Jha, Ken-ichi Nomura, Aiichiro Nakano, Priya Vashishta, Rajiv K. Kalia
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a generative AI-based approach for forecasting oil production from multi-well oilfields. By leveraging time series forecasting techniques and tailored models, the authors aim to accurately predict uncertainties and inform decision-making processes at the field scale. The approach utilizes an autoregressive model called TimeGrad and a transformer architecture variant named Informer, both specifically designed for forecasting long sequence time series data. The results show that predictions from both TimeGrad and Informer align closely with ground truth data, with the Informer demonstrating greater efficiency in forecasting oil production rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to predict how much oil will come out of wells. This is important because it helps us understand how much oil we have and how much money we’ll make from it. The authors use special computer models to forecast what will happen over time, taking into account things like changes in the rate at which oil comes out of each well. They test two different models, one called TimeGrad and another called Informer, and find that both are good but one is better than the other. |
Keywords
» Artificial intelligence » Autoregressive » Time series » Transformer
