Abstract
Model errors are ubiquitous in practical history matching problems. A common approach in the literature to accounting for model errors is to treat them as random variables following certain presumed distributions. While such a treatment renders algorithmic convenience, its underpinning assumptions are often invalid. In this work, we adopt an alternative approach, and treat model-error characterization as a functional approximation problem, which can be solved using a generic machine learning method. We then integrate the proposed model-error characterization approach into an ensemble-based history matching framework, and show that, with very minor modifications, existing ensemble-based history matching algorithms can be readily deployed to solve the history matching problem in the presence of model errors.To demonstrate the efficacy of the integrated history matching framework, we apply it to account for potential model errors of a rock physics model in 4D seismic history matching applied to the full Norne benchmark case. The numerical results indicate that the proposed model-error characterization approach helps improve the qualities of estimated reservoir models, and leads to more accurate forecasts of production data. This suggests that accounting for model errors from a perspective of machine learning serves as a viable way to deal with model imperfection in practical history matching problems.
Highlights
Quantitative analyses of real-world phenomena often involve using certain numerical models
After wards, we demonstrate the performance of the strengthened 4D seismic history matching framework through a case study in the full Norne field
We present an integrated seismic history matching (SHM) framework that consists of a few functional modules, namely, forward seismic simulations, seismic data processing and ensemblebased history matching
Summary
Quantitative analyses of real-world phenomena often involve using certain numerical models. Though, taking into account the dependence of model errors on model variables may nullify the conventional Gaussianity assumption adopted for the purpose of characterizing model errors.1 In effect, this might make the resulting history matching workflow more complicated, unless additional sources of information are assumed. The goal of the current work is to demonstrate the applicability of the MEC approach in Luo (2019) to account for potential model errors from a rock physics model (RPM) used to history-match 4D seismic data in a full Norne field case study (Lorentzen et al, 2020). We conclude the whole work with some technical discussions and thoughts for potential future work
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