[1B11-1B15] Natural resource exploitation (2)

[1B12] Agile uncertainty evaluation for field development with multiple high quality history matched models using cloud computing

○DAIKI WATANABE¹, YUTA TOSUJI¹, HIROTATSU YAMABE² (1. JX Nippon Oil & Gas Exploration Corporation, 2. Schlumberger)

Keywords:Cloud Computing, Uncertainty Evaluation, Multiple History Matching

History matching (HM) is an inverse analysis, and there are multiple combinations of variables that reproduce past production behavior. Therefore, forecasting by the model depends on the variables adjusted by HM and is subject to uncertainty. However, it is common practice to create only a few representative models to forecast production due to the limited machine power and other factors, and the uncertainty is often not adequately evaluated. To solve this problem, a total of 90 HM models were created in a short period of time using cloud computing developed by Schlumberger and the uncertainty of the production forecast results was evaluated. This presentation will provide an overview of the study, focusing on the workflow.