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.

This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.

The oil industry has recently started to deal with probabilistic approach. Risk or uncertainty analysis have become part of the petroleum engineer's job. A set of curves with the associated probability instead of one deterministic curve is provided by the reservoir engineers. In order to use reliable curves, they shall have a history matched model. Assisted History Matching usually uses optimization processes. The aim of the optimization is to find the minimum of an objective function that represents the quality of the model. In this way, one can find the best model. The keyword is exactly "best". Why to make so much effort to find the best if we know that it is still far from the truth. Indeed, the concept of "best" is not suitable for the probabilistic approach. This work discusses a functional history matching approach where an optimization process is no longer necessary. The functional history matching approach establishes that we have to look for a set of models that is above a level of quality according to the reservoir engineers. The method is quite simple. Among all possible models, we select those that have an objective function value under a pre-defined value. In this approach the discussion lies not in the optimization issues like local minimum, convergence, and rapidity, but in how the quality of the model is measured. The objective function that usually measures the quality must be very well defined. Not only to better take into account the historical data but also to be suitable to the purpose of the study. Infill drilling and new secondary recovery systems would probably require different objective functions. This work discusses the functional history matching approach coupled with uncertainty analysis. Usually very costly in terms of numerical simulations, uncertainty analysis can be done in this approach with simplified models (proxys). Different proxys were used -Surface Response Modeling (improved or not) and Artificial Neural Network. A simple synthetic case (PUNQ), and a real complex case (Brazilian onshore field) were used to illustrate the functional approach.

Geological, reservoir, economical and technological uncertainties have an effect on decision making and consequently on reserves development plans. Quantifying the impact of these uncertainties can make this process more reliable. A great difficulty to achieve this in practice is the variability and complexity of workflows available to manage uncertainty using numerical simulation.The inaccuracy, high uncertainty or lack of reliable data yields risk to the forecasting process, making the calibration of the dynamical model with the field production data indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead to acceptable solutions, especially due to the high degree of uncertainty of these attributes. A set of solutions that respect the observed data may lead to different prediction scenarios.The objective of this work is the integration of history matching with probabilistic analysis of representative scenarios. A methodology that allows the recognition of well-calibrated models within an acceptable deviation is used. This procedure helps to identify the critical uncertain parameters and their possible variation in order to estimate the representative reserve range. The goal is not to find the best deterministic match, but rather to show how the calibration process allows a mitigation of identified uncertainties.A real case based on a reservoir from Campos Basin in Brazil was used. A 14 year historical period followed by a 12 year forecast period was considered, allowing verification and validation, at a global level, of the proposed procedure in a complex dynamic model. Two different commercial softwares were used, in order to demonstrate the advantages and restrictions of each approach. Distribution variations of the responses in time were evaluated by Latin Hypercube sampling and Monte Carlo propagation on validated proxy models.The proposed methodology allows: (1) to reduce the range of possible models taking into account the observed data;(2) to identify the existent uncertainty as a function of observed data; (3) to reduce the uncertainty range of critical reservoir parameters; (4) to increase confidence in production forecast. One contribution of this work is to present a quantitative approach for increasing the reliability of reservoir simulation as an auxiliary tool in decision making processes in order to reduce the associates risk and to maximize development opportunities. IntroductionThe field used for this work is located on Campos Basin in Brazil, about 80 km from the shore. The field is constituted from siliciclastic turbidite reservoirs under a water depth between 300 and 800 m. The main sand reservoir has good petrophysical characteristics (roughly 27% porosity and 3000 mD permeability) and also good-quality oil (29° API and 2.1 cp viscosity).The field has a high sand / shale ratio and several normal faults, resulting in blocks with good hydraulic communication. The main production block is divided in three stratigraphic zones...

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