Abstract

History matching is an important reservoir engineering process whereby the values of uncertain attributes of a reservoir model are changed to find models that have a better chance of reproducing the performance of an actual reservoir. As a typical inverse and ill-posed problem, different combinations of reservoir uncertain attributes lead to equally well-matched models and the success of a history-matching approach is usually measured in terms of its ability to efficiently find multiple history-matched models inside the search space defined by the parameterization of the problem (multiple-matched models have a higher chance of better representing the reservoir performance forecast). While studies on history-matching approaches have produced remarkable progress over the last two decades, given the uniqueness of each reservoir’s history-matching problem, no strategy is proven effective for all cases, and finding alternative, efficient, and effective history-matching methodologies is still a research challenge. In this work, we introduce a learning-from-data approach with path relinking and soft clustering to the history-matching problem. The proposed algorithm is designed to learn the patterns of input attributes that are associated with good matching quality from the set of available solutions, and has two stages that handle different types of reservoir uncertain attributes. In each stage, the algorithm evaluates the data of all-available solutions continuously and, based on the acquired information, dynamically decides what needs to be changed, where the changes shall take place, and how such changes will occur in order to generate new (and hopefully better) solutions. We validate our approach using the UNISIM-I-H benchmark, a complex synthetic case constructed with real data from the Namorado Field, Campos Basin, Brazil. Experimental results indicate the potential of the proposed approach in finding models with significantly better history-matching quality. Considering a global misfit quality metric, the final best solutions found by our approach are up to 77% better than the corresponding initial best solutions in the datasets used in the experiments. Moreover, compared with previous work for the same benchmark, the proposed learning-from-data approach is competitive regarding the quality of solutions found and, above all, it offers a significant reduction (up to 30 × less) in the number of simulations.

Highlights

  • Reservoir simulation is an important tool in the petroleum industry

  • Since the UNISIM-I-H benchmark was publicly released by Avansi and Schiozer (2015b), it has been used to validate different history-matching approaches: Mesquita et al (2015), Bertolini et al (2015), Oliveira et al (2017), Maschio and Schiozer (2016, 2018), Cavalcante et al (2017), and Soares et al (2018). Each of these approaches has its specificities and eventually uses a parameterization that is different from the one adopted in this work (Sect. 3.1), most of them use the same normalized quadratic deviation (NQD) indicator (Sect. 2.2) to assess the quality of solutions generated during the history-matching process. In this third round of experiments, our goal is to compare the results obtained with our learning-from-data approach with the best results reported by five of the previous methodologies applied to the UNISIM-I-H benchmark, considering two aspects: (i) the total number of simulations used by each approach; (ii) the quality of solutions found, measured by the percentage of final models having a maximum NQD value of no more than 5, 10 or 20

  • We applied our approach to the complex UNISIM-I-H benchmark, and the results show the potential the strategy has towards finding solutions with good matching quality, geologically consistent appearance, and acceptable forecast capability while using a small number of simulations

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Summary

Introduction

Reservoir simulation is an important tool in the petroleum industry. Combining data from different areas, such as mathematics, physics, geology, and reservoir engineering, it involves the construction of numerical reservoir modelsIn a reservoir model, the physical space of a real reservoir is represented by a grid—a set of discrete cells (grid blocks)—and there are two main types of input data (Ertekin et al 2001): geological data, that describe reservoir rock properties (e.g., porosity and permeability) and how they are distributed along the reservoir; and engineering data, that1 3 Vol.:(0123456789)Journal of Petroleum Exloration and Production Technology (2021) 11:3045–3077 provide the description of the static and dynamic properties related to the reservoir’s fluids.The reservoir model input data is gathered and validated by a multidisciplinary team. Combining data from different areas, such as mathematics, physics, geology, and reservoir engineering, it involves the construction of numerical reservoir models. The physical space of a real reservoir is represented by a grid—a set of discrete cells (grid blocks)—and there are two main types of input data (Ertekin et al 2001): geological data, that describe reservoir rock properties (e.g., porosity and permeability) and how they are distributed along the reservoir; and engineering data, that. Except for seismic data, direct measures of reservoir properties are only available at well locations, which accounts for less than 1% of the reservoir’s volume (Schulze-Riegert and Ghedan 2007). The data used in the construction of a reservoir model is only the best interpretation from engineers and geologists of the available data. Many uncertainties are still present, and an initial reservoir model can rarely reproduce the real reservoir performance

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