Reducing uncertainty on land subsidence modeling prediction by a sequential data-integration approach. Application to the Arlua off-shore reservoir in Italy

In recent years, the awareness about the critical importance of correctly dealing with uncertainty in numerical models is spreading over an increasing number of application fields, including geomechanics for energy resources. Sources of uncertainty are related for instance to the mathematical constitutive law that describes the deep rock behavior, the geomechanical parameters and the geological nature of the investigated field. Data assimilation techniques take advantage of the increasing availability of in -situ measurements in order to account for and reduce uncertainties in modeling outcomes. Recently, a comprehensive workflow for a stochastic analysis of land subsidence has been proposed. It combines a geomechanical model with successive steps of model diagnostic and data assimilation techniques, like x2-test, Red Flag and Ensemble Smoother. Successive steps require increasing computational effort, but provide more accurate outcomes. The application of the workflow is repeated in time when new measurements become available so that the model is dynamically updated and the uncertainties are reduced. The objective of this study is to apply and validate the workflow on the Arlua reservoir. The outcome is the development and experimentation of a comprehensive geomechanical model that automatically integrates real measurements and progressively reduces the prediction uncertainties by a continuous training in time. The application confirms the effectiveness of the proposed integrated approach and proves its robustness and quality in a complex off-shore reservoir in Italy.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

In recent years, there has been an increasing awareness of the importance of correctly dealing with uncertainty in numerical models, particularly in the field of geomechanics for energy resources. This study proposes a comprehensive workflow that combines a geomechanical model with model diagnostic and data assimilation techniques to reduce uncertainties in land subsidence modeling. The workflow is applied and validated on the Arlua reservoir, demonstrating its effectiveness in reducing prediction uncertainties in a complex offshore reservoir in Italy.