Enhancing dynamic interpretation at the Valhall Field by correlating well activity to 4D seismic signatures

A review and analysis of post‐stack time‐lapse time‐shifts has been carried out that covers published literature supplemented by in‐house datasets available to the authors. Time‐shift data are classified into those originating from geomechanical effects and those due to fluid saturation changes. From these data, conclusions are drawn regarding the effectiveness of post‐stack time‐shifts for overburden and reservoir monitoring purposes. A variety of field examples are shown that display the range and magnitude of variation for each class of application. The underlying physical mechanisms creating these time‐shifts are then described, and linked to a series of generic and field‐specific rock physics calculations that predict their magnitudes. These calculations serve as a guide for practitioners wishing to utilize this information on their own datasets. Conclusions are drawn regarding the reliability of this attribute for monitoring purposes, and the extent to which further development is required and how it should be reported by authors.

“…An interesting effect was observed in the Valhall field where compaction appeared to cancel out time‐shifts due to gas out of solution (Huang et al . ).…”

Section: The Magnitude Of 4d Seismic Time‐shiftsmentioning

confidence: 97%

Section: Time-shifts From Saturation Changesmentioning

confidence: 99%

Section: Pressure Versus Saturationmentioning

confidence: 99%

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Review Paper: Post‐stack 4D seismic time‐shifts: Interpretation and evaluation

MacBeth

Mangriotis

Amini

2018

“…Compared with conventional 3D seismic data interpretation, the key point of 4D seismic interpretation is to perform discrepancy comparison (Alvarez and MacBeth 2014;Huang et al 2011;Li 2003) Variance attribute, amplitude attribute, and impedance attribute are used in the 4D seismic interpretation. Variance cube is obtained by calculating variance value between a seismic trace and average seismic traces in seismic data volume in order to highlight the impact of formation lateral variation and reflect the discontinuous change of formation.…”

Section: Seismic Data Interpretationmentioning

confidence: 99%

An application of 4D seismic monitoring technique to modern coal mining

2016

A B S T R A C TTo study the impact of modern coal mining on the overlying formation, a full-lifecycle four-dimensional seismic monitoring study has been carried out. Four seismic data campaigns have been performed using flexi-bin geometry with square bins, with total duration of 171 days. The four seismic datasets have been processed with the same processing workflow and parameters; major problems such as statics correction, signal-to-noise ratio, resolution, and consistency processing are addressed taking into account the geological features of the research area. This guarantees that remaining four-dimensional differences between the time-lapse datasets show mostly geological factors due to the coal mining and effects such as surface subsidence. Our four-dimensional seismic monitoring of modern coal mining shows that mined and unmined areas have significant zoning characteristics; coal mining has a direct impact on the overlying formation. The mining leads to obvious event subsidence, which reflects that overlying formations undergo subsidence during the mining process. The overlying formation appears as two zones called caving zone and fractured zone. We determine the fault dip of the overlying formation at one end of the working face to be 56°or so by calculation and conversion. We also see that, during the coal mining process, over time, the overlying formation has a self-recovery capability, which gradually strengthens from the roof siltstone upward to the Aeolian sandstone near the surface. The stability of 20-m coal pillars between working faces displays a strengthening trend and remains safe during the mining process due to both coal seam supporting and formation compaction effects.

“…Despite some excellent examples of vertical sections from 4D seismic data (Clifford et al 2003;Røste et al 2009), mapbased attributes remain in common use for 4D seismic interpretation as they combine the accepted visual benefits of lateral continuity with ease of use and robustness. Indeed, from maps of a suitable attribute derived at a top reservoir or perhaps a known fluid contact, it is relatively straightforward to build an understanding of the causal relationship between well production and recovery and time-lapsed seismic signatures (Huang et al 2011). From past examples, it is now relatively well known that pore pressure increase due to injection into an isolated reservoir compartment or an increase in gas saturation due to a drop below a bubble point may lead to a reservoir softening (impedance reduction) and a corresponding change on the mapped seismic amplitudes (Parr, Marsh and Griffin 2000).…”

Section: Introductionmentioning

confidence: 99%

An insightful parametrization for the flatlander's interpretation of time‐lapsed seismic data

Alvarez

MacBeth

2013

A B S T R A C TAn approximation is developed that allows mapped 4D seismic amplitudes and timeshifts to be related directly to the weighted linear sum of pore pressure and saturation changes. The weights in this relation are identified as key groups of parameters from a petroelastic model and include the reservoir porosity. This dependence on groups of parameters explains the inherent non-uniqueness of this problem experienced by previous researchers. The proposed relation is of use in 4D seismic data feasibility studies and inversion and interpretation of the 4D seismic response in terms of pore pressure and water saturation changes. A further result is drawn from analysis of data from the North Sea and West Africa, which reveals that the relative interplay between the effects of pore pressure and saturation changes on the seismic data can be simplified to the control of a single, spatially variant parameter C S /C P . Combining these results with those from published literature, we find that C S /C P = 8 appears to be a generality across a range of clastic reservoirs with a similar mean porosity. Using this C S /C P value, an in situ seismic-scale constraint for the rock stress sensitivity component of the petroelastic model is constructed considering this component carries the largest uncertainty.