Response Surface Designs for Scenario Management and Uncertainty Quantification in Reservoir Production

Jourdan, Astrid; Zabalza-Mezghani, I.

doi:10.1023/b:matg.0000048801.30621.04

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…The application of response-surface modeling in reservoir-engineering studies has been largely reported for uncertainty analysis of reservoir performance (Chu 1990;Egeland et al 1992;Damsleth et al 1992;Friedmann et al 2003;Jourdan and Zabalza-Mezghani 2004), well-scheme optimization (Güyagüler 2002;Landa and Güyagüler 2003), and history matching (White and Royer 2003;Feraille and Roggero 2004). A proxy model becomes very useful, especially when the direct evaluation of the system is either impossible or too expensive and time consuming.…”

Section: Polynomial Response Surfacementioning

confidence: 99%

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

Sedighi¹

2010

SPE Journal

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Seismic history matching is the process of modifying a reservoir simulation model to reproduce the observed production data in addition to information gained through time-lapse (4D) seismic data. The search for good predictions requires that many models be generated, particularly if there is an interaction between the properties that we change and their effect on the misfit to observed data. In this paper, we introduce a method of improving search efficiency by estimating such interactions and partitioning the set of unknowns into noninteracting subspaces. We use regression analysis to identify the subspaces, which are then searched separately but simultaneously with an adapted version of the quasiglobal stochastic neighborhood algorithm. We have applied this approach to the Schiehallion field, located on the UK continental shelf. The field model, supplied by the operator, contains a large number of barriers that affect flow at different times during production, and their transmissibilities are highly uncertain. We find that we can successfully represent the misfit function as a second-order polynomial dependent on changes in barrier transmissibility. First, this enables us to identify the most important barriers, and, second, we can modify their transmissibilities efficiently by searching subgroups of the parameter space. Once the regression analysis has been performed, we reduce the number of models required to find a good match by an order of magnitude. By using 4D seismic data to condition saturation and pressure changes in history matching effectively, we have gained a greater insight into reservoir behavior and have been able to predict flow more accurately with an efficient inversion tool. We can now determine unswept areas and make better business decisions.

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Section: Polynomial Response Surfacementioning

confidence: 99%

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

Sedighi¹

2010

SPE Journal

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“…Under Gaussian assumptions, θ could be estimated by maximum likelihood (Mardia and Marshall,7 and leads usually to a local maximum (Warnes and Ripley, 1987 [16]). In this paper, we select the correlation parameter which minimizes the empirical integrated mean squared error,…”

Section: Descriptionmentioning

confidence: 99%

How to repair a second-order surface for computer experiments by Kriging

Jourdan¹

2009

Chemometrics and Intelligent Laboratory Systems

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: Designs and analysis of computer experiments have been widely investigated in the past decade. Two approaches seem adopted by engineers, the standard response surface methodology and the Kriging method. This paper describes and compares them briefly in order to present a methodology for computer experiments based on these two classical methods. The objective is to combine the advantages of the two approaches, notably the low cost of the standard experimental designs and the accurate response surface of the Kriging method. The use and the efficiency of this tool is demonstrated empirically through examples.

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“…This advanced risk analysis approach still needs for further development and validation on key topics such as for instance non-linear modeling for specific responses, the development of specific designs for discrete parameters, the integration of production data to reduce the uncertainties ranges on technical uncontrollable parameters, … Some interesting results have already been obtained in these topics 18,19,20 and further validation on real field cases should be encouraged. Table 11: comparison of the optimal production scheme for the three different cases…”

Section: General Conclusionmentioning

confidence: 99%

Advanced Risk Analysis Approach for Optimization of a Water Injection Program -Illustration on a Brazilian Field Case

Manceau

Feraille

Zabalza-Mezghani

et al. 2005

SPE Latin American and Caribbean Petroleum Engineering Conference

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper presents an advanced risk analysis approach that has been applied on a Brazilian field case to manage and quantify the reservoir uncertainties during production forecasting and production scheme optimization. The oil field (named PBR) is located offshore Brazil. The PBR reservoir consists of a complex lithology, including mainly turbiditic sandstones interbedded by shales and marls. The workflow of the PBR reservoir modeling from the geostatistical lithofacies model, built using the non-stationary truncated gaussian method, to the flow model (about 24000 active cells) has been implemented in a fully integrated chain. The field was considered in this hypothetical study at its appraisal stage. Main challenge consisted in selecting an optimal water injection scheme to maximize oil production while minimizing water production and maintaining pressure over the 15 years of production, taking into account the overall major sources of uncertainties in the reservoir. Advanced risk analysis methods were used with the following objectives: -Determine the impact of the main uncertainties on the field production forecasts versus time and versus geographical locations of production and injection wells -Optimize the water injection phase to maintain pressure over time, while producing maximum oil and minimum water in a 15-years period.-Quantify the impact of main uncontrollable reservoir uncertainties on production forecasts and fluid distribution maps versus time for the optimal water injection program selected in order to identify potential risks with non-drained zones and pressure drops. At each step, either classical or optimal experimental designs were used to be able to perform an accurate analysis at a minimum cost in terms of reservoir simulation and interpretation. Results show that this innovative methodology can be successfully applied on complex real cases to quantify the risk associated with the main reservoir uncertainties during production forecasts and to optimize the water injection. In particular, this example illustrates how some innovative interpretation tools can be combined with experimental design methodology to perform advanced risk analysis for reservoir management.

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Response Surface Designs for Scenario Management and Uncertainty Quantification in Reservoir Production

Cited by 12 publications

References 4 publications

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

Faster Convergence in Seismic History Matching by Dividing and Conquering the Unknowns

How to repair a second-order surface for computer experiments by Kriging

Advanced Risk Analysis Approach for Optimization of a Water Injection Program -Illustration on a Brazilian Field Case

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