Petroleum reservoir uncertainty mitigation through the integration with production history matching

Becerra, Gustavo Gabriel; Maschio, Célio; Schiozer, Denis José

doi:10.1590/s1678-58782011000200005

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Although a reasonable history period, the field presents some difficulties to be matched. The parameterization is based on the remaining uncertainties in the petrophysical properties (mainly horizontal, vertical, and relative permeability) and the influence of the aquifer (Becerra 2007). …”

Section: Applicationsmentioning

confidence: 99%

A Methodology To Reduce Uncertainty Constrained to Observed Data

Maschio

Schiozer

Filho

et al. 2009

SPE Reservoir Evaluation &Amp; Engineering

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This paper presents a new method to deal with uncertainty mitigation by using observed data, integrating the uncertainty analysis and the history-matching processes. The proposed methods are robust and easy to use, and offer an alternative to traditional historymatching methods. The main characteristic of the method is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The main objective is the integration of uncertainty analysis with history matching, providing a natural manner to make predictions under reduced uncertainty. Three methods are proposed: (1) probability redistribution, (2) elimination of attribute levels, and (3) redefinition of attribute values. To test the results of the proposed approach, we investigated three reservoir examples. The first one is a synthetic and simple case; the second one is a synthetic but realistic case; and the third one is a real reservoir from the Campos basin of Brazil. The results presented in the paper show that it is possible to conduct an integrated study of uncertainty analysis and history matching. The main contribution of this work is to present a practical way to increase the reliability of prediction through reservoir-simulation models that incorporate uncertainty analysis in the history period and provide reliable reservoir-simulation models for prediction forecast.

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Section: Applicationsmentioning

confidence: 99%

A Methodology To Reduce Uncertainty Constrained to Observed Data

Maschio

Schiozer

Filho

et al. 2009

SPE Reservoir Evaluation &Amp; Engineering

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“…Maschio et al (2009) applied sensitivity analysis with probability redistribution of discretized levels in the history matching problem. Similarly, Becerra et al (2011) used sensitivity analysis in an approach to reduce the uncertainty in a history matching problem. Kassenov et al (2014) showed an application of sensitivity analysis with design of experiments in the Tengiz field.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

Costa

Maschio

Schiozer

2019

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

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History matching for naturally fractured reservoirs is challenging because of the complexity of flow behavior in the fracture-matrix combination. Calibrating these models in a history-matching procedure normally requires integration with geostatistical techniques (Big Loop, where the history matching is integrated to reservoir modeling) for proper model characterization. In problems involving complex reservoir models, it is common to apply techniques such as sensitivity analysis to evaluate and identify most influential attributes to focus the efforts on what most impact the response. Conventional Sensitivity Analysis (CSA), in which a subset of attributes is fixed at a unique value, may over-reduce the search space so that it might not be properly explored. An alternative is an Iterative Sensitivity Analysis (ISA), in which CSA is applied multiple times throughout the iterations. ISA follows three main steps: (a) CSA identifies Group i of influential attributes (i = 1, 2, 3, …, n); (b) reduce uncertainty of Group i, with other attributes with fixed values; and (c) return to step (a) and repeat the process. Conducting CSA multiple times allows the identification of influential attributes hidden by the high uncertainty of the most influential attributes. In this work, we assess three methods: Method 1 – ISA, Method 2 – CSA, and Method 3 – without sensitivity analysis, i.e., varying all uncertain attributes (larger searching space). Results showed that the number of simulation runs for Method 1 dropped 24% compared to Method 3 and 12% to Method 2 to reach a similar matching quality of acceptable models. In other words, Method 1 reached a similar quality of results with fewer simulations. Therefore, ISA can perform as good as CSA demanding fewer simulations. All three methods identified the same five most influential attributes of the initial 18. Even with many uncertain attributes, only a small percentage is responsible for most of the variability of responses. Also, their identification is essential for efficient history matching. For the case presented in this work, few fracture attributes were responsible for most of the variability of the responses.

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“…The methods to change the discretized uncertainties were probability redistribution, level elimination, and redefinition of attribute values. Becerra et al (2008), Maschio et al (2009) and Becerra et al (2011) presented some advances of this methodology by applying it to more complex reservoir models, and showed the benefits of considering uncertainties in the production forecasting. Risso et al (2011) compared the precision of simulation results for risk analysis applications by using different techniques for dealing with reservoir uncertainties and generating model combinations.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach to Uncertainty Reduction with a Probabilistic and Multi-Objective History Matching

2015

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The petroleum industry has a high demand for production forecasting under uncertainties, which is performed through probabilistic approaches. Although these approaches have been used very often in recent years, one of the challenges to their application is the development of a methodology with multiple scenarios that honor the dynamic data available (history matching procedures). Many works, which propose different probabilistic history matching approaches have been published in the literature but are based on methods that demand a very high number of reservoir simulation runs. Thus, to make this type of application feasible, these methods rely on the application of proxy models, which, however, are not able to capture the full physical behavior of the model. Different from those methods, this work proposes a methodology for uncertainties reduction of reservoir properties integrated with an assisted history matching procedure, in a multi-objective approach that does not employ proxies.The methodology proposed is an extension of a procedure previously published by our group, which has shown to be an efficient way to perform probabilistic history matching. At each iteration of the process, the model uncertainties are combined through a robust sampling technique, which originates a set of model realizations that are then simulated. The results are evaluated for all Objective Functions (OF), namely, the quadratic deviations of all well rates and pressures. This work proposes the use of quantitative tools to perform the matching: (1) indexes that quantify the matching quality for each OF and allow prioritizing the worst OF to be matched and (2) a bi-dimensional matrix that supports the identification of sources to the highest deviations and gradual model properties updating. At the end of the procedure, good models (which must present satisfactory matching for all OF) are filtered and further employed in production forecasting under uncertainties.The methodology is applied to a synthetic model (UNISIM-I-H) based on the Namorado field in Brazil. The results show not only the gradual improvement of all OF during the matching procedure but also and, more importantly, how the reservoir uncertainties are updated based on the tools proposed here.The proposed indicators make the application systematic and can be used for automatizing the process. The methodology is able to deal with complex cases, especially those involving several wells and uncertainties with non-linear interaction among them. The developments provide robustness to the application, resulting in a more reliable production forecast and management of the field.

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Petroleum reservoir uncertainty mitigation through the integration with production history matching

Cited by 9 publications

References 25 publications

A Methodology To Reduce Uncertainty Constrained to Observed Data

A Methodology To Reduce Uncertainty Constrained to Observed Data

Evaluation of an uncertainty reduction methodology based on Iterative Sensitivity Analysis (ISA) applied to naturally fractured reservoirs

A Systematic Approach to Uncertainty Reduction with a Probabilistic and Multi-Objective History Matching

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