Quantification of Uncertainty by Combining Forecasting with History Matching

J. Braz. Soc. Mech. Sci. & Eng.

2011

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.

Section: Literaturementioning

confidence: 99%

Petroleum reservoir uncertainty mitigation through the integration with production history matching

Becerra

J. Braz. Soc. Mech. Sci. & Eng.

2011

“…Assessing uncertainty in production forecasts by constructing a synthetic reservoir model, Varela et al (2006) verified that the seismicamplitude data did not uniformly improve the variability of the predictions of water-breakthrough time. Alvarado et al (2005) proposed a procedure that includes likelihood of the forecast period on the basis of the quality of the match along with the objective function (OF). In other words, for a given number of history-match runs, weighted mean and standard deviations of the corresponded-forecast runs are calculated and this information is used to guide the history match and, at the same time, to generate confidence intervals of the prediction profiles.…”

Section: Introductionmentioning

confidence: 99%

A Methodology To Reduce Uncertainty Constrained to Observed Data

SPE Reservoir Evaluation &Amp; Engineering

Filho

et al. 2009

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.

“…Moreover, even with access to the code, the implementation is not trivial. Alvarado et al (2005) proposed a procedure that includes likelihood of the forecast period based on the quality of the match along with the objective function. In other words, for a given number of history match runs, weighted mean a standard deviation of the corresponded forecast runs are calculated and this information are used to guide the history match and at the same time to generate confidence intervals of the prediction profiles.…”

Section: Introductionmentioning

confidence: 99%

A Methodology To Quantify the Impact of Uncertainties in the History-Matching Process and in the Production Forecast

Proceedings of SPE Annual Technical Conference and Exhibition

Filho³

2005

TX 75083-3836, U.S.A., fax 01-972-952-9435. ProposalTraditional history matching provides a unique deterministic reservoir model. However, it is well known that history matching is a complex inverse problem that can have multiple solutions. Therefore, a unique model is not sufficient to guarantee reliable production forecasting. This work presents a quantitative analysis of the uncertainties of the reservoir attributes integrated to the history matching process. Instead of use a set of deterministic attributes and changes them to adjust to the observed data, as in conventional way, a probabilistic analysis is incorporated. In this methodology, probability density functions are discretized in given number of levels. Comparing models response (simulation data) with observed data, there is a reduction of the probabilities of the scenarios that do not present a good match; some models can be discarded during the process. The major difficulty in this process is to determine how to redistribute the occurrence probability of the attributes of the remaining models. In this paper, a criterion to make the rearrangement of the probabilities is established. The main objective of the paper is to present a consistent methodology to integrate history matching and the impact of uncertainties in the production forecasting.Firstly, the methodology is validated with synthetic cases derived from Tenth Comparative Solution Project (SPE 10, Model 2). Then, the proposed methodology is applied to a real case from an offshore field. In the two applications, the conventional history matching process is also applied in order to compare the results and evaluate the reliability of a unique model prediction against a set of most probable models predictions.The main contribution of this work is to increase the reliability of prediction through reservoir simulation models and to show the necessity of incorporating uncertainties in the history matching.