Gradient Method and Bayesian Formalism Application to Petrophysical Parameter Characterization

Roggero, F.; Guérillot, Dominique

doi:10.3997/2214-4609.201406885

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…All of these tests have revealed the importance of this method within the particular framework of weIl-tests. Extensions to multiphase flows have been carried out [8]. The inversion of production data is possible and it is undoubtably a great help in the construction of a reservoir model.…”

Section: Discussionmentioning

confidence: 99%

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Gradients Method Constrained by Geological Bodies for History Matching

Rahon¹,

Blanc²,

Guérillot³

1996

ECMOR v - 5th European Conference on the Mathematics of Oil Recovery

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Conventional gradient methods have already been applied to reservoir engineering for matching the history of former field performances. The key point of these methods is to select the best areal reservoir zoning for reduction of the amount of reservoir parameters to be identified. In this paper we propose a zoning based on reservoir lithofacies, thus making a more natural than geographical choiee. By introducing gradients to numrmze an objective function that measures the difference between observed and simulated well pressure responses, we can effectively achieve the inversion of petrophysical lithofacies parameters. By fixing the value of petrophysical parameters, the influence of the geometry can be studied by varying the geologie body's dimensions. Several examples of inversion are given at the end of the artiele to illustrate the effectiveness of this gradient method.

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Section: Discussionmentioning

confidence: 99%

“…in which : Different efficient optimization algorithms can be used when analytical gradients are supplied. We performed these tests with the Gauss-Newton, Powell and Levenberg-Marquardt algorithms [7,8].…”

Section: Case 3 • Multilayer Modelmentioning

confidence: 99%

Gradients Method Constrained by Geological Bodies for History Matching

Rahon¹,

Blanc²,

Guérillot³

1996

ECMOR v - 5th European Conference on the Mathematics of Oil Recovery

Self Cite

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“…For this option, we use an optimization package offering several optimization algorithms: Powell, Levenberg-Marquardt, Gauss-Newton, Steepest-Descent, BFGS, 8,9 . The lift gas rates are defined as a time varying table where each value is a parameter.…”

Section: Global Optimization Packagementioning

confidence: 99%

Comparisons of Various Algorithms for Gas-lift Optimization in a Coupled Surface Network and Reservoir Simulation

Samier

2010

SPE EUROPEC/EAGE Annual Conference and Exhibition

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Gas lift is used for gas-liquid two phase producers to boost the production. Increasing gas liquid ratio GLR reduces the average fluid mixture density in the well connections and therefore reduces the hydrostatic pressure drop and hence reducing the bottomhole pressure resulting in a higher production rate or a longer individual well production period. This technique is also used for deep offshore fields injecting the gas at the toe of the FPSO risers located at the seafloor.But as the lift gas supply is increased further, friction pressure losses in the tubing or the riser become more important and the production rate peaks then starts to decrease. Also high gas flowrates may induce corrosion problems, and the total amount of available gas-lift is limited. Therefore an optimal gas-lift rate exists for a maximum production rate or more generally a maximum production profit.Various gas lift optimization algorithms have been proposed in literature for optimizing gas-lift to individual wells or within a group of wells, very few are suitable for long-term reservoir development studies with gas-lift injection within a network (FPSO riser toe for example).Commercial simulators such as Eclipse, VIP, Nexus, … provide internal optimization tool which are convenient for gas-lift at wells connections. Optimizing gas-lift within a network with additional gas rate inside flowlines or risers becomes much more complicated since the THP limits of all the other wells in the network are affected. Each time a lift gas increment is added or substracted, the whole network must be rebalanced with the appropriate lift gas rate in order to recomputed the change in the field oil production rate. The paper reviews some of the optimization methods available in commercial coupled surface network and reservoir simulators such as Resolve -Gap-Eclipse, Nexus, Avocet, … Several optimization algorithms linked to a surface facility model coupled to an open reservoir simulator prototype have been also applied (Ensemble based method, Steepest-Descent, Gauss-Newton, BFGS, ..) and compared to previous commercial software solutions.Results of the comparisons are presented on a surface network model coupled to a reservoir simulator where the objective is to optimize the gas-lift allocations at riser toes.One of the test cases based on a modified SPE 9 comparative test is fully documented and allows the readers to compare results with their own optimization algorithm. Introduction Problem statementThe optimization problem is to maximize daily hydrocarbon production by optimally selecting lift-gas rates subject to the pressure and rate constraints of surface facilities. The problem has been addressed by several authors in the oil industry. Fang and Lo 1 proposed a linear programming technique to allocate lift-gas rates. Hepguler et al. 2 uses a sequential programming (SQP) optimization algorithm. Wang et al. 3 continued on the Fang and Lo's work. The optimization step is invoked at the Newton iteration level of a reservoir simulator and becomes a part of...

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“…Methods using multiple models include Monte Carlo simulation, the extreme scenarios method (Nepveu, 2000), and Bayesian inversion (Roggero and Guerillot, 1996). Floris et al (2001) presented a comparative study of different methods used by several research groups asked to estimate the uncertainty in production forecasts from a synthetic case study.…”

Section: Downloaded By [The Aga Khan University] At 22:26 11 Decembermentioning

confidence: 99%

“…Egberts et al (2002) presented an uncertainty quantification based on the maximum entropy method that reproduced historical data with satisfactory accuracy from 25 stochastic realizations based on 2,000 automated numerical simulations of the reservoir. Roggero and Guerillot (1996) presented a new methodology that combines Bayesian formalism with extreme scenarios to identify the extreme behavior models for a given production forecasting behavior criterion (e.g., maximum and minimum reserves) that are equally probable. The difference between these two extreme production values is used as an estimation of uncertainty.…”

Section: Downloaded By [The Aga Khan University] At 22:26 11 Decembermentioning

confidence: 99%

Quantification of Uncertainty by Combining Forecasting with History Matching

Alvarado

McVay

Lee

2005

Petroleum Science and Technology

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Quantifying uncertainty in production forecasts is critical to making good reservoir management decisions, particularly for many current investment opportunities that require intensive technology and large investments, and that may have marginal profitability indicators. Reservoir studies are conducted to support decision making, but reservoir management decisions must often be made before completion of these studies. This paper presents a new approach to reservoir studies that combines production forecasting with history matching. The approach provides preliminary production forecasts much earlier in reservoir studies. More importantly, the approach provides estimates of uncertainty associated with the forecasts. This is accomplished by using the mismatch of history match runs to weight corresponding forecast runs.We illustrate application of the method to the 8-Sand reservoir in the Green Canyon 18 field, Gulf of Mexico. We observed that, as the accuracy of the model increased during the history match, the uncertainty of forecasted reserves decreased and the distribution of reserves stabilized.Early forecasts and associated estimates of uncertainty provided by our new method can be quite valuable to management in making investment decisions. 446Alvarado et al.

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Gradient Method and Bayesian Formalism Application to Petrophysical Parameter Characterization

Cited by 7 publications

References 5 publications

Gradients Method Constrained by Geological Bodies for History Matching

Gradients Method Constrained by Geological Bodies for History Matching

Comparisons of Various Algorithms for Gas-lift Optimization in a Coupled Surface Network and Reservoir Simulation

Quantification of Uncertainty by Combining Forecasting with History Matching

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