A two‐invariant pseudoelastic model for reservoir compaction

Spiezia, Nicolò; Ferronato, Massimiliano; Janna, Carlo; Teatini, Pietro

doi:10.1002/nag.2704

Cited by 9 publications

(8 citation statements)

References 52 publications

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“…In this work, an in-house developed code is used. Details on the numerical formulation and implementation are given in [27,31,41]. This code has been widely validated and used in several real-world applications, e.g., [22,31,46,47,55].…”

Section: Numerical Forward Modelmentioning

confidence: 99%

A novel methodological approach for land subsidence prediction through data assimilation techniques

et al. 2021

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Anthropogenic land subsidence can be evaluated and predicted by numerical models, which are often built over deterministic analyses. However, uncertainties and approximations are present, as in any other modeling activity of real-world phenomena. This study aims at combining data assimilation techniques with a physically-based numerical model of anthropogenic land subsidence in a novel and comprehensive workflow, to overcome the main limitations concerning the way traditional deterministic analyses use the available measurements. The proposed methodology allows to reduce uncertainties affecting the model, identify the most appropriate rock constitutive behavior and characterize the most significant governing geomechanical parameters. The proposed methodological approach has been applied in a synthetic test case representative of the Upper Adriatic basin, Italy. The integration of data assimilation techniques into geomechanical modeling appears to be a useful and effective tool for a more reliable study of anthropogenic land subsidence.

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Section: Numerical Forward Modelmentioning

confidence: 99%

A novel methodological approach for land subsidence prediction through data assimilation techniques

et al. 2021

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“…the χ 2 -test, is suggested. This procedure allows for a preliminary validation of the ensemble based on the mismatch between the model outcomes and the available measurements that, for linear problems, follows a χ 2 distribution with degrees of freedom equal to the number of measurements (Tarantola, 2005). The χ 2test, as already used in Chen andOliver (2013), Fokker et al (2016) and Oliver and Alfonzo (2018), provides here an indication of the most suitable ensemble for the successive and more expensive DA steps of the procedure.…”

Section: Methodological Approachmentioning

confidence: 99%

Numerical simulation of land subsidence above an off-shore Adriatic hydrocarbon reservoir, Italy, by Data Assimilation techniques

et al. 2020

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The numerical prediction of land subsidence above producing reservoirs can be affected by a number of uncertainties, related for instance to the deep rock constitutive behavior, geomechanical properties, boundary and forcing conditions, etc. The quality and the amount of the available observations can help reduce such uncertainties by constraining the numerical model outcome and providing more reliable estimates of the unknown governing parameters. In this work, we address the numerical simulation of land subsidence above a producing hydrocarbon field in the Northern Adriatic, Italy, by integrating the available monitoring data in the computational model with the aid of Data Assimilation strategies. A preliminary model diagnostic analysis, i.e. the χ 2 -test, allows for identifying the most appropriate forecast ensemble. Then, a Bayesian approach, i.e. the Red Flag technique, and a smoother formulation, i.e. the Ensemble Smoother, provide a significant reduction of the prior uncertainties. The experiment developed on a real-world gas field confirms that the integration of monitoring observations with classical geomechanical models is a valuable approach to improve the reliability of land subsidence predictions and to exploit in a systematic way the increasing amount of available measurement records.

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“…A way to validate the ensemble is the χ 2 -test, as used in Chen and Oliver (2013), Fokker et al (2016) and Oliver and Alfonzo (2018). This procedure is based on the fact that, for linear problems, the minimum of the mismatch between the model result and the available measurements produces a χ 2 distribution with degrees of freedom equal to the number of measurements (Tarantola, 2005).…”

Section: Methodological Approachmentioning

confidence: 99%

Blending measurements and numerical models: a novel methodological approach for land subsidence prediction with uncertainty quantification

et al. 2020

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Abstract. The use of numerical models for land subsidence prediction above producing hydrocarbon reservoirs has become a common and well-established practice since the early '90s. Usually, uncertainties in the deep rock behavior, which can affect the forecast capability of the models, have been taken into account by running multiple simulations with different constitutive laws and mechanical properties. Then, the most uncertain parameters were calibrated to reproduce available subsidence measurements. The objective of this work is to propose a novel methodological approach for land subsidence prediction and uncertainty quantification by integrating the available monitoring information in numerical models using ad hoc Data Assimilation techniques. The proposed approach allows to: (i) train the model with the available data and improve its accuracy as new information comes in, (ii) quantify the prediction uncertainty by providing confidence intervals and probability measures instead of deterministic outcomes, and (iii) identify the most appropriate rock constitutive model and geomechanical parameters. The methodology is tested in synthetic models of production from hydrocarbon reservoirs. The numerical experiments show that the proposed approach is a promising way to improve the effectiveness and reliability of land subsidence models.

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A two‐invariant pseudoelastic model for reservoir compaction

Cited by 9 publications

References 52 publications

A novel methodological approach for land subsidence prediction through data assimilation techniques

A novel methodological approach for land subsidence prediction through data assimilation techniques

Numerical simulation of land subsidence above an off-shore Adriatic hydrocarbon reservoir, Italy, by Data Assimilation techniques

Blending measurements and numerical models: a novel methodological approach for land subsidence prediction with uncertainty quantification

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