Simultaneous History-Matching Approach by Use of Reservoir-Characterization and Reservoir-Simulation Studies

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

2018

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Accurately characterizing fractures is complex. Several studies have proposed reducing uncertainty by incorporating fracture characterization into simulations, using a probabilistic approach, to maintain the geological consistency, of a range of models instead of a single matched model. We propose a new methodology, based on one of the steps of a general history-matching workflow, to reduce uncertainty of reservoir attributes in naturally fractured reservoirs. This methodology maintains geological consistency and can treat many reservoir attributes. To guarantee geological consistency, the geostatistical attributes (e.g., fracture aperture, length, and orientation) are used as parameters in the history matching. This allows us to control Discrete Fracture Network attributes, and systematically modify fractures. The iterative sensitivity analysis allows the inclusion of many (30 or more) uncertain attributes that might occur in a practical case. At each uncertainty reduction step, we use a sensitivity analysis to identify the most influential attributes to treat in each step. Working from the general history-matching workflow of Avansi et al. (2016), we adapted steps for use with our methodology, integrating the history matching with geostatistical modeling of fractures and other properties in a big loop approach. We applied our methodology to a synthetic case study of a naturally fractured reservoir, based on a real semi-synthetic carbonate field, offshore Brazil, to demonstrate the applicability in practical and complex cases. From the initial 18 uncertain attributes, we worked with only 5 and reduced the overall variability of the Objective Functions. Although the focus is on naturally fractured reservoirs, the proposed methodology can be applied to any type of reservoir.

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A new methodology to reduce uncertainty of global attributes in naturally fractured reservoirs

Costa

Maschio

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

2018

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“…As mentioned previously, uncertainties were divided into two groups, scalar and petrophysical. Avansi and Schiozer (2015) and Avansi et al (2016) performed a careful modeling of the benchmark. Table 2 shows the uncertain attributes considered and their initial parameterization.…”

Section: Application: Unisim-i-hmentioning

confidence: 99%

A novel localization scheme for scalar uncertainties in ensemble-based data assimilation methods

Soares

Maschio

J Petrol Explor Prod Technol

2019

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History matching, also known as data assimilation, is an inverse problem with multiple solutions responsible for generating more reliable models for use in decision-making processes. An iterative ensemble-based method (Ensemble Smoother with Multiple Data Assimilation-ES-MDA) has been used to improve the solution of history-matching processes with a technique called distance-dependent localization. In conjunction, ES-MDA and localization can obtain consistent petrophysical images (permeability and porosity). However, the distance-dependent localization technique is not used to update scalar uncertainties, such as relative permeability; therefore, the variability for these properties is excessively reduced, potentially excluding plausible answers. This work presents three approaches to update scalar parameters while increasing the final variability of these uncertainties to better scan the search space. The three approaches that were developed and compared using a benchmark case are: binary correlation coefficient (BCC), based on correlation calculated by ES-MDA through cross-covariance matrix C f MD (BCC-C MD); BCC, based on a correlation coefficient between the objective functions and scalar uncertainties (R) (BCC-R); and full correlation coefficient (FCC). We used the work of Soares et al. (J Pet Sci Eng 169:110-125, 2018) as a base case to compare the approaches because although it showed good matches with geologically consistent petrophysical images, it generated an excessive reduction in the scalar parameters. BCC-C MD presented similar results to the base case, excessively reducing the variability of the scalar uncertainties. BCC-R increased the variability in the scalar parameters, especially for BCC with a higher threshold value. Finally, FCC found many more potential answers in the search space without impairing data matches and production forecast quality.

“…Meanwhile, the comparison steps are supported by production history data, where each modification in the simulation models is quantified and diagnosed during the assisted history matching. The deviations of production history data throughout initial and updated simulation models can be evaluated either qualitatively (graph analysis) or quantitatively using the normalized quadratic deviations with sign (NQDS) as described in Avansi, Maschio and Schiozer (). NQDS is a normalized quadratic error that is used to quantify the misfit between all historical and simulation objective functions based on acceptance criteria.…”

Section: Reservoir Model Updating Workflowmentioning

confidence: 99%

Quantitative integration of 3D and 4D seismic impedance into reservoir simulation model updating in the Norne Field

Maleki

Davólio

Geophysical Prospecting

2018

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The ultimate goal of reservoir simulation in reservoir surveillance technology is to estimate long‐term production forecasting and to plan development and management of petroleum fields. However, maintaining reliable reservoir models which honour available static and dynamic data, involve inherent risks due to the uncertainties in space and time of the distribution of hydrocarbons inside reservoirs. Recent applications have shown that these uncertainties can be reduced by quantitative integration of seismic data into the reservoir modelling workflows to identify which areas and reservoir attributes of the model should be updated. This work aims using seismic data to reduce ambiguity in calibrating reservoir flow simulation model with an uncertain petro‐elastic model, proposing a circular workflow of inverted seismic impedance (3D and 4D) and engineering studies, with emphasis on the interface between static and dynamic models. The main contribution is to develop an updating procedure for adjusting reservoir simulation response before using it in the production forecasting and enhance the interpretive capability of reservoir properties. Accordingly, the workflow evaluates consistency of reservoir simulation model and inverted seismic impedance, assisted by production history data, to close the loop between reservoir engineering and seismic domains. The methodology is evaluated in a complex, faulted, sandstone reservoir, the Norne benchmark field, where a significant reservoir behaviour understanding (about the static and dynamic reservoir properties) is obtained towards the quantitative integration of seismic impedance data. This leads to diagnosis of the reservoir flow simulation reliability and generation of an updated simulation model consistent with observed seismic and well production history data, as well as a calibrated petro‐elastic model. Furthermore, as Norne Field is a benchmark case, this study can be considered to enrich the discussions over deterministic or probabilistic history matching studies.