Optimizing 4D fluid imaging

McInally, A.; Redondo-López, T.; Garnham, J.; Kunka, J.; Brooks, A. D.; Hansen, L. Stenstrup; Barclay, Frazer; Davies, David

doi:10.1144/1354-079302-537

Cited by 18 publications

(16 citation statements)

References 21 publications

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“…It is widely understood that reservoir heterogeneity greatly exceeds the ability of reservoir models to predict where geofluids move during oil and gas production, or how groundwater moves in aquifers and in candidate waste repositories. In the hydrocarbon industry, rapidly increasing use of time‐lapse seismic imaging reservoir fluid movement is enabling more rational production decisions to improve recovery efficiency and to lower operating costs (Francis & Pennington 2001; Oil & Gas Industry Task Force 2001; de Waal & Calvert 2003; McInally et al 2003; Parker et al 2003). Using time‐lapse imaging of injected fluids to map groundwater aquifers is a likely future development (Davis et al 2003; Li 2003).…”

Section: Introductionmentioning

confidence: 99%

Physical model for the downhole orbital vibrator (DOV) - I. Acoustic and borehole seismic radiation

Leary

Walter

2005

Geophysical Journal International

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S U M M A R YThe downhole orbital vibrator (DOV) applies the Vibroseis principle to borehole seismic sourcing. Accelerations by an internal rotating eccentric mass excite cylindrical pressure waves converted at the wellbore to seismic waves essentially within 20 • -30 • of the plane normal to the borehole. DOV pressure waves in open water are quantitatively described by a rotating point force radiating acoustic waves with displacement amplitude u(r ) ≈ 1/2 u 0 γ R 1 /r , where R 1 = π 2 ρ dov /ρ water a 2 /λ 2 ≈ 1 mm is the DOV effective size for DOV radius a ≈ 5 cm, length ≈ 1 m and acoustic radiation wavelength λ ≈ 10 m, and u 0 ≈ 1 µm and γ x are, respectively, the frequency-independent DOV displacement amplitude and the direction cosine of the observer relative to the instantaneous point-force axis in the plane of the rotating point force. Crosswell seismic radiation amplitude, spectrum and angular dependence are quantitatively described by acoustic wave diffraction at a slit with DOV axial cross-section 2a , followed by conversion to seismic waves at the wellbore. Seismic wave displacement amplitudes u(r ) ≈u 0 γ R 2 /r scale with effective radius R 2 ≈ 2a /λ ≈ 1 cm. The frequency dependence of R 2 is observed as linear frequency enhancement of the seismic wavelet spectrum relative to the source wavelet spectrum. DOV borehole P-and S-wave production peaks strongly in the plane of DOV rotation, with converted S waves both parallel to and transverse to the borehole axis. The small effective source sizes R 1 ≈ 1 mm and R 2 ≈ 1 cm at operational frequencies 50-350 Hz imply that DOV motion in a borehole is dynamically decoupled from the borehole wall. Dynamic decoupling allows DOV borehole seismic correlation wavelets to be quantitatively modelled in terms of a stable kinematic relation between source and sensor motion. Acoustic and seismic data rule out dipole-source action associated with claims for shear traction and primary S-wave radiation from boreholes. The stable linear kinetics of DOV acoustic action in borehole fluids produces (i) useful crosswell seismic signals at offsets to 650-800 m, (ii) negligible tube waves and (iii) stable seismic wavelets suited to in situ time-lapse seismic imaging of fluid migration fronts in crustal reservoirs.

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Section: Introductionmentioning

confidence: 99%

Physical model for the downhole orbital vibrator (DOV) - I. Acoustic and borehole seismic radiation

Leary

Walter

2005

Geophysical Journal International

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“…Time-lapse seismic monitoring (4D) seismic surveys were introduced from the early1990s (MacLeod et al 1999;Koster et al 2000;Hansen et al 2001;de Waal & Calvert 2003;Hubans et al 2003;Marsh et al 2003;McInally et al 2003;Rose et al 2011). During production, the pore fluids, pore pressure and temperature change in the reservoir, and these affect the seismic response.…”

Section: Enabling Technologies In a Mature Basinmentioning

confidence: 99%

Tertiary deep-marine reservoirs of the North Sea region: an introduction

McKie

Rose²,

Hartley

et al. 2015

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“…The monitor surveys were dedicated to 4D seismic acquisition (Boyd-Gorst et al 2001;McInally et al 2003). The monitor surveys were dedicated to 4D seismic acquisition (Boyd-Gorst et al 2001;McInally et al 2003).…”

Section: Nelson Fieldmentioning

confidence: 99%

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Stephen

Kazemi

2014

Geophysical Prospecting

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Updating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data. We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match. The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.

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Optimizing 4D fluid imaging

Cited by 18 publications

References 21 publications

Physical model for the downhole orbital vibrator (DOV) - I. Acoustic and borehole seismic radiation

Physical model for the downhole orbital vibrator (DOV) - I. Acoustic and borehole seismic radiation

Tertiary deep-marine reservoirs of the North Sea region: an introduction

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

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