Compaction and 4‐D time strain at the Genesis Field

The estimation of time-lapse time shifts between two, or several, repeated seismic surveys has become increasingly popular over the past eighteen years. These time shifts are a reliable and informative seismic attribute that can relate to reservoir production. Correction for these time shifts or the underlying velocity perturbations and/or subsurface displacement in an imaging sense also permits accurate evaluation of time-lapse amplitudes by attempting to decouple the kinematic component. To date, there are approximately thirty methods for time-shift estimation described in the literature. We can group these methods into three main families of mathematical development, together with several miscellaneous techniques. Here we detail the underlying bases for these methods, and the acknowledged benefits and weaknesses of each class of method highlighted. We illustrate this review with a number of time-lapse seismic examples from producing fields. No method is necessarily superior to the others, as its selection depends on ease of implementation, noise characteristics of the field data, and whether the inherent assumptions suit the case in question. However, cross-correlation stands out as the algorithm of choice based on the Pareto principle and waveform inversion the algorithm delivering best resolution. This is a companion study to the previous review of time-shift magnitudes and a discussion of their rock physics basis.

Section: Time‐shift Measurement and Methodsmentioning

confidence: 99%

\begin{matrix} true \\ right C_{j, k + 1} & = & left C_{j, k} - w_{k - N} b_{k - N} m_{k - N + j} \\ left + w_{k + 1 + N} b_{k + 1 + N} m_{k + 1 + N + j} . \end{matrix}

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Section: Time‐shift Measurement and Methodsmentioning

confidence: 99%

Review Paper: Methods of measurement for 4D seismic post‐stack time shifts

MacBeth

Amini

Izadian

2020

“…Numerous (over 10) methods are available to compute post-stack time-shifts (the reader is referred to Ji 2017, for a review and comparison of different time-shift measurement methods). We use Rickett et al (2006) 1D fast cross-correlation algorithm, which is a robust and straightforward time-shift technique that can recover time shifts without reduction in resolution due to oversmoothing (Ji 2017). Selection of optimum window length and maximum correlation lag is typically a trial-and-error process; there exists a trade-off between smoothing (large window) and having a noisy time-shift result (small window), whereas the maximum lag should be in the range of the expected timeshift (unless a cascaded approach of bulk plus fine time-shift computation is adopted).…”

Section: T I M E -L a P S E S E I S M I C S I G N A T U R Ementioning

confidence: 99%

Scale effects on modelling the seismic signature of gas: results from an outcrop analogue

Mangriotis

MacBeth

Briceno

2018

Numerous examples of reservoir fields from continental and marine environments involve thin‐bedded geology, yet, the inter‐relationship between thin‐bedded geology, fluid flow and seismic wave propagation is poorly understood. In this paper, we explore the 4D seismic signature due to saturation changes of gas within thin layers, and address the challenge of identifying the relevant scales and properties, which correctly define the geology, fluid flow and seismic wave propagation in the field. Based on the study of an outcrop analogue for a thin‐bedded turbidite, we model the time‐lapse seismic response to fluid saturation changes for different levels of model scale, and explore discrepancies in quantitative seismic attributes caused by upscaling. Our model reflects the geological complexity associated with thin‐bedded turbidites, and its coupling to fluid flow, which in turn affects the gas saturation distribution in space, and its time‐lapse seismic imprint. Rock matrix and fluid properties are modelled after selected fields to reproduce representative field models with realistic impedance contrasts. In addition, seismic modelling includes multiples, in order to assess their contribution in seismic propagation through thin gas layers. Our results show that multiples could contribute significantly to the measured amplitudes in the case of thin‐bedded geology. This suggests that forward/inverse modelling involving the flow simulation and seismic domains used in time‐lapse seismic interpretation should account for thin layers, when these are present in the geological setting.

“…Since travel-time shifts represent the cumulative travel-time differences along the ray path, the seismic travel-time shift attribute will not be a local measure of physical perturbations in the way that reflection amplitudes are. However, the so-called time strain attribute provides an instantaneous (or interval) travel-time difference measurement (Rickett et al 2006) and can be calculated by taking the temporal derivative of time shifts ( t 0 /t 0 ). Landrø and Stammeijer (2004) introduced the zero-offset relative travel-time shift change for a single layer to describe the combined effects of fractional changes in layer thickness and seismic velocity for normal incidence.…”

Section: E S T I M a T I N G V E L O C I T Y C H A N G E U S I N G 1 mentioning

confidence: 99%

“…However, the so-called time strain attribute provides an instantaneous (or interval) travel-time difference measurement (Rickett et al 2006) …”

Section: Estimating Velocity Change Using 1d Transformmentioning

confidence: 99%

Analysis of time-lapse travel-time and amplitude changes to assess reservoir compartmentalization

Angus

Clark

et al. 2015

Fluid depletion within a compacting reservoir can lead to significant stress and strain changes and potentially severe geomechanical issues, both inside and outside the reservoir. We extend previous research of time-lapse seismic interpretation by incorporating synthetic near-offset and full-offset common-midpoint reflection data using anisotropic ray tracing to investigate uncertainties in timelapse seismic observations. The time-lapse seismic simulations use dynamic elasticity models built from hydro-geomechanical simulation output and a stress-dependent rock physics model. The reservoir model is a conceptual two-fault graben reservoir, where we allow the fault fluid-flow transmissibility to vary from high to low to simulate non-compartmentalized and compartmentalized reservoirs respectively. The results indicate time-lapse seismic amplitude changes and traveltime shifts can be used to qualitatively identify reservoir compartmentalization. Due to the high repeatability and good quality of the time-lapse synthetic dataset, the estimated traveltime shifts and amplitude changes for near-offset data match the true model subsurface changes with minimal errors. A 1D velocity-strain relation was used to estimate the vertical velocity change for the reservoir bottom interface by applying zero-offset time-shifts from both the near-offset and full-offset measurements.For near-offset data, the estimated P-wave velocity changes were within 10% of the true value.However, for full-offset data, time-lapse attributes are quantitatively reliable using standard time-lapse seismic methods when an updated velocity model is used rather than the baseline model.