The Marlim Field: Incorporating Time-Lapse Seismic in the Assisted History Matching

Brito, Daniel; Moraes, Rafael; Emerick, Alexandre A.

doi:10.2118/137763-ms

Cited by 10 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…seismic, electromagnetics) provide valuable information that can considerably improve the assimilation process (Emerick et al, 2007b). For instance, over the past decades, an increasing number of seismic monitoring cases has been observed (Ullmann De Brito et al, 2010. One of the main advantages of time-lapse seismic data is its ability to approximate the pressure/fluid distribution inside the reservoir.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Method For Data Assimilation

Moraes

Hajibeygi

2018

Proceedings

View full text Add to dashboard Cite

In data assimilation problems, various types of data are naturally linked to different spatial resolutions (e.g. seismic and electromagnetic data), and these scales are usually not coincident to the subsurface simulation model scale. Alternatives like down/upscaling of the data and/or the simulation model can be used, but with potential loss of important information. To address this issue, a novel Multiscale (MS) data assimilation method is introduced. The overall idea of the method is to keep uncertain parameters and observed data at their original representation scale, avoiding down/upscaling of any quantity. The method relies on a recently developed mathematical framework to compute adjoint gradients via a MS strategy. The fine-scale uncertain parameters are directly updated and the MS grid is constructed in a resolution that meets the observed data resolution. The advantages of the technique are demonstrated in the assimilation of data represented at a coarser scale than the simulation model. The misfit objective function is constructed to keep the MS nature of the problem. The regularization term is represented at the simulation model (fine) scale, whereas the data misfit term is represented at the observed data (coarse) scale. The performance of the method is demonstrated in synthetic models and compared to down/upscaling strategies. The experiments show that the MS strategy provides advantages 1) on the computational side -expensive operations are only performed at the coarse scale; 2) with respect to accuracy -the matched uncertain parameter distribution is closer to the "truth"; and 3) in the optimization performance -faster convergence behaviour due to faster gradient computation. In conclusion, the newly developed method is capable of providing superior results when compared to strategies that rely on the up/downscaling of the response/observed data, addressing the scale dissimilarity via a robust, consistent MS strategy.

show abstract

Section: Introductionmentioning

confidence: 99%

A Multiscale Method For Data Assimilation

Moraes

Hajibeygi

2018

Proceedings

View full text Add to dashboard Cite

show abstract

Local adaptive parameterization for the history matching of 3D seismic data

Veiga

Gervais

2011

Comput Geosci

View full text Add to dashboard Cite

Matching seismic data in assisted history matching processes can be a challenging task. One main idea is to bring flexibility in the choice of the parameters to be perturbed, focusing on the information provided by seismic data. Local parameterization techniques such as pilot-point or gradual deformation methods can be introduced, considering their high adaptability. However, the choice of the spatial supports associated to the perturbed parameters is crucial to successfully reduce the seismic mismatch. The information related to seismic data is sometimes considered to initialize such local methods. Recent attempts to define the regions adaptively have been proposed, focusing on the mismatch between simulated and reference seismic data. However, the regions are defined manually for each optimization process. Therefore, we propose to drive the definition of the parameter support by performing an automatic definition of the regions to be perturbed from the residual maps related to the 3D seismic data. Two methods are developed in this paper. The first one consists in clustering the residual map with classification algorithms. The second method proposes to drive the generation of pilot point locations in an adaptive way. Residual maps, after proper normalization, are considered as probability density functions of the pilot point locations. Both procedures lead to a complete adaptive and highly flexible perturbation technique for 3D seismic matching. A synthetic study based on the PUNQ test case is introduced to illustrate the potential of these adaptive strategies.

show abstract

Maximum likelihood classification for facies inference from reservoir attributes

Veiga

Ravalec

2012

Comput Geosci

View full text Add to dashboard Cite

A cluster analysis methodology is developed to recover facies realizations from observed reservoir attributes. A maximum likelihood estimator allows us for identifying the most probable underlying facies using a spatial clustering algorithm. In seismic characterization, this algorithm can yield relevant geological models for subsequent history-matching studies. In history-matching procedures, it provides informative facies maps as well as starting points for further studies.

show abstract

The Marlim Field: Incorporating Time-Lapse Seismic in the Assisted History Matching

Cited by 10 publications

References 8 publications

A Multiscale Method For Data Assimilation

A Multiscale Method For Data Assimilation

Local adaptive parameterization for the history matching of 3D seismic data

Maximum likelihood classification for facies inference from reservoir attributes

Contact Info

Product

Resources

About