The method uses multiple repeated seismic monitor surveys shot across a field undergoing production and recovery. The interpretation is map-based and uses intra- reservoir time-shift maps extracted from pre-computed time-shift volumes and 4D amplitude maps calculated as the root-mean-square (RMS) average along the top
reservoir interface. The magnitude of the mapped 4D (monitor-baseline) seismic signals at different monitor times are collected at specific locations in the reservoir where any of the dynamic effects (pressure increase/decrease, water saturation or gas saturation changes) are verified to be the over-riding influence and considered to be large enough 4D signals. This is then cross-plotted against known pressure or saturation changes at these locations. Following this, the amplitude and time-shift sensitivity is quantified as described in Equations 2-23 and 2-26, respectively (see section 2.3). In the numerator of Equation 2-23, the 4D amplitude response is expressed as a fractional change weighted by the baseline seismic amplitude. This allows for a direct comparison of sensitivity across different fields where amplitudes are of different scales. This could also be the case for a particular field where monitor and a matched baseline seismic data have been processed with different amplitude scales, compared to other baseline/monitor datasets for the same field. Likewise, in Equation 2-26, the intra- reservoir time-shift is normalised by the average thickness of the reservoir formation. As this study is concerned with the response of the reservoir rock, the normalisation removes the influence of thicknesses in the time-shifts (since thick reservoirs will appear to have bigger time-shifts than thin reservoirs). This therefore allows for a balanced comparison across different fields which have different reservoir thicknesses.
The 4D interpretation workflow is given in Figure 3-2. Well production data (co-located in space and time with the seismic response) together with reservoir model depth- averaged maps of simulated pressure and saturation changes at the same dates are used to validate the 4D seismic signatures for a causal effect. Simulator-to-seismic modelling can also be performed as a data QC/validation step to further assess the 4D seismic response (Amini, 2014). For consistency and clarity, it is best to maintain the same polarity for the 4D seismic response across monitor times i.e. a consistent polarity for amplitudes and a consistent polarity for time-shifts. For each monitor time and for each selected area, both attribute maps are compared to ensure that their 4D responses are in agreement. The mapped 4D amplitude signals are used to guide the interpretation of the intra-reservoir time-shift maps.
Sensitivity calibration requires that strong saturation or pressure-induced signals across monitor surveys are interpretable and that the magnitude of the saturation or pressure changes at the selected locations are known. Pore pressure changes can be obtained
from three sources: bottom-hole-pressure (BHP) build-up tests for wells (injectors and producers) perforated in the reservoir formation, repeat-formation-tester (RFT) logs, Drill-stem test (DST) shut-in pressures and/or depth-averaged maps from a history- matched fluid-flow simulation model. These pressure data sources carry their own uncertainties, with the measured (historical) BHP, RFT or DST pressure generally preferred to simulated data (Beaumont et al., 1999). Saturation changes are usually obtained as depth-averaged maps from the simulation model or can be inferred from historic gas-oil-ratios (for gas saturation changes) and water-cut (for water saturation changes) in producing wells in the reservoir formation (for example, Floricich, 2006).
Figure 3-2 Conceptual 4D seismic interpretation workflow (modified after Johnston, 2013) for the map- based calibration of the reservoir’s sensitivity in 4D seismic data. The workflow is repeated for each 4D seismic data across the monitor times, with 4D amplitudes and intra-reservoir time-shifts maps interpreted side-by-side. For selecting areas for calibration, the map-based signal-to-noise (SNR or S/N) ratio in 4D seismic data is the inverse of the noise-to-signal ratio (N/S) which is calculated as shown in Appendix A.2. Is it a valid 4D signal? Interpret what the presence of a 4D signal implies
Validate with tie to wells and production data Interpret what the lack of a 4D signal implies. Document why the changes are invalid Yes No No Yes Assess that other production effects are minimal Select areas with 4D SNR > 3 for local calibration by polygon mapping Cross-plot the mapped 4D signals against the co-located corresponding pressure or saturation changes Yes No Document the other effects mainly controlling the 4D signal Quantify sensitivity to the various effects interpreted Is the 4D signal mainly due to pressure or saturation? Are there amplitude changes or time-shifts?
The uncertainty in the quantification of sensitivity comes from the 4D seismic data and the measured/simulated dynamic production data. The uncertainty in 4D seismic data is simply given by a measure of the data non-repeatability, and the signal-to-noise ratio, SNR, (or noise-to-signal ratio, N/S) can also be computed based on non-repeatability noise, NRMS (see Appendix A.2). For a confident interpretation, only pressure or saturation dominant areas with 4D signal-to-noise ratios greater than the value of 3 are used for sensitivity estimation; a quantitative criterion suggested in Behrens et al. (2002). For the dynamic pressure or saturation data, if simulated data are used (due to a lack of data i.e. historical well BHPs or repeat logs), the uncertainty is estimated using the percentage error between historical measurements of key field production data and their simulated alternative. Key field production data are water-cut and gas-oil-ratio, which are considered as first order parameters for material balance (Dake 1997). If measured data such as historical BHPs for wells are used, the uncertainty is simply the standard deviation of the measurement error. This is regarded as unbiased observation error and the magnitude will vary from field to field depending on well operators and practices. Yin et al. (2015) provides a number of 5% based on reservoir engineering information on a specific North Sea field. This number is assumed here, as it is not available for the North Sea fields in this study.
3.2.1 Some suggestions on where best to calibrate the various effects
Although areas where pressure and saturation changes dominate are likely to be strongly field dependent, some generalisations are possible to guide this study:
(i) Inside water-flooded areas or regions of undisturbed fluid saturations - areas water-flooded at a previous monitor time are likely to show pure pressure-induced signals in subsequent monitor surveys. This result may however be biased by fractures existing around the borehole. I start by calibrating signals around water- flooded wells and expand outwards to larger water-flooded regions. Areas in the natural water or gas leg are useful as initial saturation levels are likely to remain fixed over time.
(ii) Outside areas undergoing significant saturation changes - whilst theoretically small, pressure-induced signals should still exist away from injectors
and outside the influence of a growing water-flood front. Likewise, in areas away from producers undergoing depletion but no associated gas breakout (in an oil- water system reservoir) or negligible gas condensation effect (in gas condensate reservoirs). These areas could be significant if the injection or depletion response is inside a compartment (For example, see Figure 1-7 in section 1.2). One assumption is that other changes such as temperature or salinity changes are negligible; checked from knowledge of field production mechanisms and modelling.
Saturation-induced 4D signals can be found anywhere outside the water leg. Water saturation induced signals can easily be observed around water injectors perforated in the hydrocarbon leg, sometimes around producers and also in between injectors or producers. Gas saturation induced signals are likely to concentrate around producers and/or in shallower parts of the reservoir if a new gas cap is formed or the existing gas cap expands, all dependent on pressure gradients, gravity, mobility ratios and reservoir heterogeneity. Likewise, other effects caused by temperature or salinity changes are assumed negligible. This is also validated from knowledge of field production mechanisms and modelling.