Going quantitative with 4D seismic analysis

MacBeth, Colin; Floricich, Mariano; Soldo, Juan

doi:10.1111/j.1365-2478.2006.00536.x

Cited by 59 publications

(29 citation statements)

References 17 publications

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“…MacBeth et al (2011) point out that shales have a small but finite permeability and will pressure-equilibrate with the depleting reservoir sands. Geomechanical modeling generally assumes that shales are impermeable and act as barriers.…”

Section: Some Complicationsmentioning

confidence: 99%

Practical Applications of Time-lapse Seismic Data

Johnston

2013

127

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Section: Some Complicationsmentioning

confidence: 99%

Practical Applications of Time-lapse Seismic Data

Johnston

2013

127

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“…Evaluation of a could be seen as a potential drawback of this approach and, in practice, this factor should be viewed as a free parameter to be determined by a calibration exercise (see section below on the application to the field data). If direct rock-physics calculation is not possible, another possibility is the use of a technique similar to that of MacBeth, Floricich and Soldo (2006a). The lighter and hence the more compressive the oil, the more important it is to include this factor.…”

Section: (A) Pressure-controlled 4d Seismicmentioning

confidence: 99%

“…For this analysis, three poststack migrated 3D volumes are made available from towed streamer surveys shot in 1993, 1999 and 2000. These 4D seismic data have been previously studied for field development (Parr and Marsh 2000), pressure and saturation separation (MacBeth et al 2006a), and seismic history matching ). Prior to our study, the data had been cross-equalized and processed for 4D signature extraction, and are observed to be of excellent quality and repeatability with a 4D noise/4D signal ratio of better than 10% (see Fig.…”

Section: A P P L I C At I O N T O F I E L D D Atamentioning

confidence: 99%

“…Unfortunately, examples of pressure-dominated 4D seismic are scarce at present, and for most fields there is the necessity to separate saturation and pressure changes before pursuing the permeability calculation. Various techniques have been published in an attempt to tackle this separation, such as the rock-physics-based approaches of Tura and Lumley (1999), Landrø (2001) and Ribeiro and MacBeth (2004), and the production-calibrated procedure of MacBeth et al (2006a). Unfortunately, the large size of the error bars on the inversion products arising from these methods to date prevents use of the resultant pressure maps in the scheme suggested here for application to field data, although it is possible that the mean results may be accurate.…”

Section: Should We Use Pressure or Saturation For Permeability Estimamentioning

confidence: 99%

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Extraction of permeability from time‐lapse seismic data

MacBeth¹,

Almaskeri²

2006

Geophysical Prospecting

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A B S T R A C TTwo formulae are developed for estimating horizontal permeability directly from maps of 4D seismic signatures. The choice of the formula used depends on whether the seismic is dominated by changes of pressure or saturation. However, pressure derived from time-lapse seismic, or seismic amplitudes controlled predominantly by pressure are to be preferred for optimal 'illumination' of the reservoir. The permeability is predicted to be dependent on porosity but weighted by a 4D term related to the magnitude and spatial gradient of the 4D signature. Tests performed on model-based synthetic seismic data affirm the validity and accuracy of this approach. Application to field data from the UK continental shelf reveals a large-scale permeability variation similar to the existing simulation model, but with additional fine-scale detail. The technique thus has the potential of providing extra information with which to update the simulation model. The resultant permeability estimates have been successfully ground-truthed against the results of two well tests. As non-repeatable noise in the time-lapse seismic data diminishes with improved 4D-related acquisition, it will become increasingly possible to make robust permeability estimates using this approach. I N T R O D U C T I O NIt is now widely acknowledged that time-lapse seismic data, if interpreted appropriately with due consideration of reservoir production and injection history, can provide invaluable information with which to optimize hydrocarbon recovery. This information includes, for example, the location of hydraulically conductive faults, no flow barriers or high permeability pathways, the identification of isolated compartments possibly containing bypassed oil, the position of injected fluid fronts or contacts, and general field-wide pressure and saturation changes. To date, most of these features are commonly inferred by detecting regions of brightening or dimming on difference sections or maps, and are thus essentially semiquantitative and can provide ambiguous interpretations. This style of 4D analysis is useful for reservoir management purposes, but in general may not offer sufficient accuracy to update the flow simulation model and hence to reduce uncertainty in reservoir performance predictions. A more detailed and in-depth level * E-mail: Colin.MacBeth@pet.hw.ac.uk of quantitative analysis is required, one that delivers accurate numerical estimates of absolute permeability variations, transmissibility factors, and reservoir connectivity at a variety of scales. To address this general challenge, we start by focusing here on estimating one of these parameters, i.e. reservoir permeability, which is the key flow property that controls strategies for well completion and production.The usual starting point for determining the permeability field for the flow simulation model is the core plug or welllog data. The interwell space is then populated by extrapolating between wells using an appropriate set of geostatistics, and this final reservoir model i...

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“…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). Quantitative use of 4D seismic data in history matching is an active research topic that has been explored extensively (Arenas et al, 2001, Clifford et al, 2003, MacBeth et al, 2004, Staples et al, 2005, Stephen and MacBeth, 2006, Kazemi et al, 2011, Jin et al, 2012, the main challenge being quantitatively incorporating the 4D seismic into the reservoir model (Landa, 1997, Walker et al, 2006, Jin et al, 2011. Figure 1 shows the different domains in which seismic data could be incorporated into the reservoir model as has been described previously (Stephen and MacBeth, 2006, Landa and Kumar, 2011, Alerini et al, 2014.…”

Section: Introductionmentioning

confidence: 97%

Seismic Assisted History Matching Using Binary Image Matching

Obidegwu¹,

Chassagne²,

MacBeth³

2015

Europec 2015

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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.

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Going quantitative with 4D seismic analysis

Cited by 59 publications

References 17 publications

Practical Applications of Time-lapse Seismic Data

Practical Applications of Time-lapse Seismic Data

Extraction of permeability from time‐lapse seismic data

Seismic Assisted History Matching Using Binary Image Matching

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