Improved Reservoir Modeling With Time-Lapse Seismic in a Gulf of Mexico Gas Condensate Reservoir

Waggoner, J.R.; Cominelli, A.; Seymour, R.H.

doi:10.2523/77514-ms

Cited by 12 publications

(7 citation statements)

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“…Similar quantitative work was proposed by other authors (Huang et al 1997;Waggoner et al 2002) who applied different techniques to the objective functions to minimize the error between inverted seismic impedences and those obtained from petro-elastic models. Several authors (Falcone et al 2004; Menezes and Gosselin 2006; Mezghani et al 2004) have discussed the issues related to PEM calibration and the scale at which it should be applied.…”

Section: Introductionmentioning

confidence: 71%

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Petro-Elastic Models: How Many and at What Scale?

Alfred

Atan

Hamman

et al. 2008

Europec/Eage Conference and Exhibition

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This paper describes an integrated workflow that determines the minimum number of petro elastic models required to adequately predict elastic properties for a given flow scale. The workflow involves the formulation of scale-dependent petro-elastic models (PEM) and petroelastic facies from well logs. Then through an error evaluation technique, determines the minimum number of PEMs required and the maximum vertical grid size limit for the flow scale. The validity of the fine scale PEMs across different scales is also tested in the workflow.Elastic properties of reservoir rocks can be predicted through petro-elastic models which relate fluid and intrinsic reservoir properties to elastic properties through mathematical functions or rock physics models. Although we would like to generate PEMs at flow scale, very often the PEMs are generated from logs that are consistent with core data. A PEM is lithology and rock fabric specific. In an upscaled flow model, the lithologies or textures are mixed. Fine scale PEMs may no longer adequately describe the elastic properties of the reservoir at flow and seismic scale.Prediction is improved in a flow model that integrates all available measurements. Scale inconsistencies of the measurements need to be addressed for better integration and prediction. In this paper we will show that through a unique and iterative approach, PEMs must be generated at flow scale for improved integration. The methodology has been tested with synthetic data and resulted in a better predictive flow scale model, which is consistent across all scale resolutions.

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Section: Introductionmentioning

confidence: 71%

“…Previous studies (Huang et al 1997;Waggoner et al 2002;Menezes and Gosselin 2006) employed the use of an optimization technique to adapt the fine-scale PEMs to an upscaled reservoir grid. The optimization technique involved minimizing the mismatch between observed and predicted impendences.…”

Section: Error Evaluationmentioning

confidence: 99%

Petro-Elastic Models: How Many and at What Scale?

Alfred

Atan

Hamman

et al. 2008

Europec/Eage Conference and Exhibition

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“…In addition to a preproduction seismic survey from 1996, several monitor surveys were taken. Our dataset consists of three surveys processed together : 1996: , 2002: and 2004: . The time-lapse differences 1996: -2002: and 2002: -2004 have a prominent signature due to gas evolving out of solution (Fig.…”

Section: Figure 2 Coloured Inversion Operator (Left) and Its Amplitumentioning

confidence: 99%

“…The domain of petro-elastic properties, calculated from the simulation model, has been the most popular in recent studies found in literature (Waggoner et al, 2002;Gosselin et al, 2003;Aanonsen et al, 2003;Mezghani et al, 2004;El Quair et al, 2005;Stephen et al, 2006;Roggero et al, 2007;Dong et al, 2008). Authors usually note the advantages of this domain in that no forward seismic data prediction is included in the optimization loop.…”

Section: Introductionmentioning

confidence: 99%

Assisted Seismic History Matching in Different Domains: What Seismic Data Should We Compare?

Sagitov¹,

Stephen²

2012

SPE Europec/Eage Annual Conference

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Time-lapse (4D) seismic data can be integrated into history matching by comparing predicted and observed data in various domains. These include the time domain (time traces), seismic attributes, or petro-elastic properties such as acoustic impedance. Each domain requires different modelling methods and assumptions as well as data handling workflows. The aim of this work is to investigate the degree to which the choice of domain influences theoutcome of history matching on the choice of best model and associated uncertainties. Another aspect of history matching is that long simulations often pose an obstacle for an automatic approach. In this study we use appropriately upscaled models manageable in the automatic history matching loop.We apply manual and assisted seismic history matching to the Schiehallion field. In the assisted approach, the optimization loop is driven by a stochastic algorithm, while the manual workflow is based on a qualitative comparison of 4D seismic maps. By upscaling we obtained an order of magnitude gain in performance. Accurate upscaling was ensured by thorough volume and transmissibility calculation within regions. The parameterisation of the problem is based on a pattern of seismically derived geobodies with specified transmissibility multipliers between the regions. Seismic predictions are made through petroelastic modelling, 1D convolution, coloured inversion and calculation of different attributes.We were able to achieve a reasonable match of production and 4D seismic data using coarse scale models in manual and assisted approaches. We observed that the misfit surfaces are different when working in the various seismic domains considered. Use of equivalent domains for observed and predicted data was found to give a more unique misfit response and better result.Accurate comparison of predicted and observed 4D seismic data in different domains is necessary for tackling nonuniqueness of the inverse problem and hence reducing the uncertainty of field development predictions.

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“…Step 7 has been described previously 4 , including application to this same case study 5 . It should be noted, however, that the other steps, namely analysis, reprocessing, and attribute extraction and evaluation, were critically important in improving the seismic data quality and generating meaningful 4D images.…”

Section: D Optimized Reprocessing Of Base and Monitor Surveysmentioning

confidence: 99%

Improved Reservoir Modeling With Time-Lapse Seismic in a Gulf of Mexico Gas Condensate Reservoir

Waggoner¹,

Cominelli²,

Seymour

2002

SPE Asia Pacific Oil and Gas Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractTime-lapse 3D, or 4D, seismic has been tried in several fields to date, with some good case studies published to demonstrate the utility of the 4D seismic information. While another 4D case study would be useful, this paper describes 2 novel aspects of a recent application in the Gulf of Mexico. First, the target reservoir contains a gas condensate fluid under primary depletion, so pressure, rather than saturation, changes create the observed 4D acoustic response. Further, the primary impact of the pressure change is to the fluid composition as the initially dense gas phase lightens as condensate drops out below the dew point. The result was a 3.6% change in acoustic impedance predicted in the feasibility study.Second, the 4D seismic result was used to constrain an optimized history matching procedure along with the production data.After describing the seismic history matching method used, the paper will discuss the changes to the reservoir model that resulted. For comparison, some results using a production data only history matching procedure are also presented. While the neither set of history matching results should be considered unique, they do give some insight into the structure of the reservoir that should be considered for optimal reservoir management, and provide an indication of the impact of 4D information in history matching.

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Improved Reservoir Modeling With Time-Lapse Seismic in a Gulf of Mexico Gas Condensate Reservoir

Cited by 12 publications

References 0 publications

Petro-Elastic Models: How Many and at What Scale?

Petro-Elastic Models: How Many and at What Scale?

Assisted Seismic History Matching in Different Domains: What Seismic Data Should We Compare?

Improved Reservoir Modeling With Time-Lapse Seismic in a Gulf of Mexico Gas Condensate Reservoir

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