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<p>There is a gap in ensemble-based techniques for generating prior models suitable for data assimilation, while maintaining geological consistency and properly assessing and mitigating uncertainties. This work proposes a semi-automated approach to build an ensemble of prior models calibrated with the observed production data while maintaining geological consistency. The methodology consists of: (1) generating the first ensemble of models (base ensemble)—not calibrated with observed production data—with enough variability and encompassing the observed production data, (2) re-parameterizing uncertainties by combining the normalized quadratic distance with sign (NQDS) indicator with Gaussian kernel density estimation (KDE) and (3) iterating the previous step until a considerable number of approved models (models within the confidence interval for the NQDS of each objective function) are achieved within the full ensemble of models. The methodology is applied to a giant Brazilian pre-salt field. The base ensemble consists of 200 models combining static and dynamic uncertainties and reproducing models with enough variability to encompass the observed production data. After two iterations, by combining the NQDS indicator with Gaussian KDE, an increasing of 181% of models calibrated with observed production data were achieved, when compared to the base case. Therefore, a much higher number of accurate models were obtained by rebuilding the probabilistic distribution functions (PDFs) for each uncertain variable based on observed production data. The mean permeability showed the greatest improvement as the well-log-derived permeability was based on empirical correlations with pore size. Based on a multidisciplinary effort, this work successfully improved the accuracy of prior models.</p>

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Uncertainty re-parameterization approach to calibrate simulation models with production data in a WAG injection field

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Manuel Gomes Correia,
  • Celio Maschio,
  • Alessandra Davolio,
  • Denis José Schiozer

摘要

There is a gap in ensemble-based techniques for generating prior models suitable for data assimilation, while maintaining geological consistency and properly assessing and mitigating uncertainties. This work proposes a semi-automated approach to build an ensemble of prior models calibrated with the observed production data while maintaining geological consistency. The methodology consists of: (1) generating the first ensemble of models (base ensemble)—not calibrated with observed production data—with enough variability and encompassing the observed production data, (2) re-parameterizing uncertainties by combining the normalized quadratic distance with sign (NQDS) indicator with Gaussian kernel density estimation (KDE) and (3) iterating the previous step until a considerable number of approved models (models within the confidence interval for the NQDS of each objective function) are achieved within the full ensemble of models. The methodology is applied to a giant Brazilian pre-salt field. The base ensemble consists of 200 models combining static and dynamic uncertainties and reproducing models with enough variability to encompass the observed production data. After two iterations, by combining the NQDS indicator with Gaussian KDE, an increasing of 181% of models calibrated with observed production data were achieved, when compared to the base case. Therefore, a much higher number of accurate models were obtained by rebuilding the probabilistic distribution functions (PDFs) for each uncertain variable based on observed production data. The mean permeability showed the greatest improvement as the well-log-derived permeability was based on empirical correlations with pore size. Based on a multidisciplinary effort, this work successfully improved the accuracy of prior models.