Generating Multiple History-Matched Reservoir-Model Realizations Using Wavelets

Sahni, Isha; Horne, Roland N.

doi:10.2118/89950-pa

Cited by 23 publications

(14 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has some advantage over the conventional regression approach that does not involve transformation of either of the two spaces. In the second approach, the dimension of the model space is reduced, much has been described earlier by others (Lu 2001;Lu and Horne 2000;Sahni 2006;Sahni and Horne 2006). In the third approach, which is the main focus of this paper, the dimensions of both spaces were reduced, and this was found to offer advantages over the conventional practice.…”

Section: Introductionmentioning

confidence: 86%

“…The objective is to remove artificial effects from recorded data and to extract the relevant information without substantial loss in accuracy. The second aspect focuses on the use of wavelet transform to reparameterize the model space (Lu 2001;Lu and Horne 2000;Sahni 2006;Sahni and Horne 2006). This second approach involves transforming the spatially dependent parameters into wavelet coefficients and selecting only relevant coefficients to be estimated during nonlinear regression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

Awotunde

Horne

2011

SPE Reservoir Evaluation &Amp; Engineering

Self Cite

View full text Add to dashboard Cite

With recent advances in permanent downhole technology, well-test analysts now have to deal with enormous amounts of data. Using these data requires the development of efficient algorithms that are able to extract the relevant information from the data at minimal cost. We present a multiresolution wavelet approach to estimate the spatial distribution of reservoir parameters, by performing the nonlinear least-squares regression in the wavelet domains of both time and space. Wavelet transforms have the ability to reveal important events in time signals or spatial images. Thus, we transformed both the model space and the time-series pressure data into spatial wavelet and time wavelet domains, respectively, and used a thresholding to select a subset of wavelet coefficients from each of the transformed domains. These subsets were used subsequently in nonlinear regression to estimate the appropriate description of reservoir parameters. The appropriate subset is not only smaller; the problem is also reduced to the consideration of only the important components of the measured data and only the part of the reservoir description that depends on them.As a test of the approach, we first applied the model to well-test problems involving 1D (radially composite) reservoir systems. The inverse problem was solved to estimate the distributed permeability values by performing the nonlinear least-squares regression in the wavelet domains (time and space).Results obtained were compared with those obtained from the conventional nonlinear-regression approach, using all the pressure-time data and the full set of spatial reservoir parameters. The time/space wavelet approach proved to be efficient. By reducing the dimensions of the model and data spaces, the approach eliminates redundancy in the reservoir description and in the data set. Significantly, the approach reveals the true number of reservoir parameters that can be appropriately estimated from a given data set and also reveals which components of the full data set are active in constraining the reservoir model. Thus, the approach provides a good means to integrate different data properly while avoiding the inclusion of irrelevant data during nonlinear regression.

show abstract

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

Awotunde

Horne

2011

SPE Reservoir Evaluation &Amp; Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we use a special type of sequential estimation-multiscale estimation, see [1,7,8,19,25,30,37] (a related estimation strategy is use of the wavelet transform, where wavelet coefficients are used as model parameters. Thresholding the wavelet coefficients then leads to a reduction in the number of parameters to be estimated.…”

Section: Sequential Coarse-scale Estimation-amementioning

confidence: 99%

“…Thresholding the wavelet coefficients then leads to a reduction in the number of parameters to be estimated. For more details, we refer to, e.g., [26,30]). In ordinary multiscale estimation (OME) [8,25], each parameter subregion will be divided into 2 l (l denotes the spatial dimension) new subregions at each successive estimation.…”

Section: Sequential Coarse-scale Estimation-amementioning

confidence: 99%

Impact of time-lapse seismic data for permeability estimation

Feng¹,

Mannseth

2010

Comput Geosci

View full text Add to dashboard Cite

We consider the impact of using time-lapse seismic data in addition to production data for permeability estimation in a porous medium with multiphase fluid flows, such as a petroleum reservoir under water-assisted production. Since modeling seismic wave propagation in addition to modeling fluid flows in the reservoir is quite involved, it is assumed that the time-lapse seismic data have already been inverted into fluid saturation differences (pseudoseismic data). Because an inversion process often leads to considerable error growth, we will consider pseudoseismic data with large uncertainties. The impact of pseudoseismic data is assessed through permeability estimation with and without such data and through application of some uncertainty measures for the estimated parameters. A multiscale algorithm is used for the parameter estimations, so that potential differences in attainable permeability resolution will be easily revealed. The numerical examples clearly indicate that the permeability estimation problem is stabilized at a higher level of resolution when pseudoseismic data are applied in addition to production data, even if the pseudoseismic data have large associated uncertainties. Use of the parameter uncertainty measures confirm these results.

show abstract

“…Over the past years, production data (oil rates, water rates, gas rates, pressure) has been the main historical data available, however, four dimensional (4D) seismic data is now considered a major dynamic input for history matching. That a model is matched to production data is not a sufficient condition for it to make improved predictions (Sahni and Horne, 2006), the model needs to integrate all available data as well as the geologists interpretation of the reservoir in order to provide the most representative reservoir model or models (Landa, 1997, Landa and Horne, 1997, Wang and Kovscek, 2002. The need to monitor fluid displacement is a great challenge that has been successfully overcome with the use of 4D seismic technology (Hatchell et al, 2002, Lygren et al, 2002, Waggoner et al, 2002, Vasco et al, 2004, Portella and Emerick, 2005, Huang and Lin, 2006, Emerick et al, 2007, Kazemi et al, 2011, which is the process of repeating 3D seismic surveys over a producing reservoir in time-lapse mode (Kretz et al, 2004, Avansi andSchiozer, 2011).…”

Section: Introductionmentioning

confidence: 99%

Seismic Assisted History Matching Using Binary Image Matching

Obidegwu¹,

Chassagne²,

MacBeth³

2015

Europec 2015

View full text Add to dashboard Cite

This paper presents a history matching scheme that has been applied to production data and time lapse seismic data. The production data objective function is calculated using the conventional least squares method between the historical production data and simulation predictions, while the seismic objective function uses the Hamming distance between two binary images of the gas distribution (presence of gas (1) or absence of gas (0)) sequenced over the different acquisition times. The technique is applied to a UKCS (United Kingdom Continental Shelf) field that has deep-water tertiary turbidite sands and multiple stacked reservoirs defining some degree of compartmentalisation. Thirty five parameters are perturbed in this history match, they can be classified as volumetric parameters (net-to-gross, pore volume), transmissibility parameters (permeability, transmissibility), and end points of the relative permeability curves (critical saturation points). An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process. It is found that permeability and critical gas saturation are key parameters for achieving a good history match, and that the volumetric parameters are not significant for this match in this particular reservoir. We also observe that matching only to production data marginally improves the seismic match, whilst matching to only seismic data improves the fit to production data. Combining both sets of data delivers an improvement for the production data and seismic data, as well as an overall reduction in the uncertainties. A unique feature of this technique is the use of the Hamming distance metric for seismic data history matching analysis, as this circumvents the use of the uncertain petroelastic model. This approach is easy to implement, and also helps achieve an effective global history match.

show abstract

Generating Multiple History-Matched Reservoir-Model Realizations Using Wavelets

Cited by 23 publications

References 10 publications

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

A Multiresolution Analysis of the Relationship Between Spatial Distribution of Reservoir Parameters and Time Distribution of Well-Test Data

Impact of time-lapse seismic data for permeability estimation

Seismic Assisted History Matching Using Binary Image Matching

Contact Info

Product

Resources

About