Name
Abstract | ||
|---|---|---|
We present a novel approach for planning the development of hydrocarbon fields, taking into account the sequential nature of well drilling decisions and the possibility to react to future information. In a dynamic fashion, we want to optimally decide where to drill each well conditional on every possible piece of information that could be obtained from previous wells. We formulate this sequential drilling optimization problem as a POMDP, and propose an algorithm to search for an optimal drilling policy. We show that our new approach leads to better results compared to the current standard in the oil and gas (O&G) industry. |
Year | Venue | Field |
|---|---|---|
2017 | THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Mathematical optimization,Computer science,Petroleum engineering,Drilling |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruben Rodriguez Torrado | 1 | 2 | 2.16 |
| Jesus Rios | 2 | 45 | 6.67 |
| Gerald J. Tesauro | 3 | 3130 | 1048.34 |