Joint History Matching of Production and Tracer Data Through an Iterative Ensemble Smoother: A 3D Field-Scale Case Study

Abstract

Reservoir models are often subject to uncertainties, which, if not properly taken into account, may introduce biases to the subsequent reservoir management process. To improve reliability and reduce uncertainties, it is crucial to condition reservoir models on available field datasets through history matching. There are different types of field data. Among others, production data are the most common choice, but they are subject to a major limitation of carrying relatively low value of information. On the other hand, inter-well tracer data have been shown to provide additional information about well-to-well connectivity and reservoir dynamics. However, jointly history matching production and inter-well tracer data still remains challenging due to the lack of a coherent quantitative workflow to fully integrate them. This work can be considered a step towards tackling this noticed problem. To this end, we propose a non-intrusive and derivative-free ensemble history matching workflow, in which reservoir models are more coherently conditioned on both production and inter-well tracer data with the help of a recently developed technique (adaptive localization). The workflow is successfully implemented in the Brugge benchmark case. Our study indicates that the history matching algorithm matches the production data well, regardless of the presence or absence of the tracer data. Nevertheless, by including tracer data as an additional source of information, we are able to improve the quality of the estimated reservoir models, in terms of both improved data match and reduced model discrepancies. Furthermore, we show that the proposed workflow is robust and provides a reasonably good way of uncertainty quantification. In summary, with the help of the adaptive localization scheme, we are able to address the issues of proper uncertainty quantification, and more coherent utilization of different types of field datasets.

Joint History Matching of Multiple Types of Field Data in a 3D Field-Scale Case Study

“…In practice, when the size of the observed data is much smaller than of the reservoir model parameters (e.g., N d N m ), history matching is an under-determined and high-dimensional inverse problem, which makes the history matching problem challenging, in the sense that in principle there could exist an infinite number of reservoir models matching the observed data equally well (nonuniqueness), due to the large degree of freedom (DOF) from the model side [30]. To mitigate this problem, one can either introduce a certain regularization term in the form of a least-square problem, or increase the number of field data in history matching [3]. Here we consider both aforementioned ideas, by including both a regularization term into a relevant cost function (cf.…”

“…In our previous work [3], we have shown that adopting both production and tracer data in the Brugge benchmark helps improve the performance of history matching, in comparison to the choice of using production data only. The current work can be considered as a follow-up study, in which we aim to further examine the impacts of 4D seismic data on history matching, and show the complexity of the joint history matching problem in the presence of multiple types of field data.…”

“…The current work can be considered as a follow-up study, in which we aim to further examine the impacts of 4D seismic data on history matching, and show the complexity of the joint history matching problem in the presence of multiple types of field data. To this end, in the current work we conduct two experiments complementary to those in [3]:…”

“…In a standard history matching process, the field data used to estimate uncertain reservoir model parameters are well production data, which include bottom hole pressure, and oil, gas, and water rates. These kinds of measurements are frequent in time and relatively cheap in terms of acquisition cost, but their usefulness is often somewhat insufficient for the purpose of reducing uncertainties in production forecasts, due to their limited spatial coverage of the reservoir [2,3]. In addition to production data, other types of field data, e.g., inter-well tracer and 4D seismic data, can be used as complementary sources of information to further reduce the uncertainties-hence improve the reliability-of reservoir models, which constitutes the focus of the current study.…”

“…Despite the appealing features of tracer and 4D seismic data, they are still often qualitatively employed for reservoir monitoring and management in the petroleum industry, but are relatively less used in a quantitative way, due to various reasons arising from both theoretical understandings and practical applications to jointly match both types of field data in a coherent way. Tracer has been incorporated into history matching by a few authors [3,[5][6][7][8][9][10]. Meanwhile, recently, several authors have demonstrated the benefits of incorporating seismic data into a history matching process [2,[11][12][13][14][15].…”