4D Time Strain and the Seismic Signature of Geomechanical Compaction at Genesis

Duranti, Luca; Hudson, Thomas; Regel, Bernard; Hodgson, Neil

doi:10.3997/2214-4609.201401441

Cited by 11 publications

(19 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a clear maximum of 15 ms. Measurements are derived from the NLI algorithm of Rickett et al . (). (b) slowdowns from a steam chamber developed as a result of steam‐assisted gravity drainage EOR in a Canadian heavy oil reservoir.…”

Section: The Magnitude Of 4d Seismic Time‐shiftsmentioning

confidence: 97%

“…Most time‐shifts have been measured on post‐stack data, predominantly using cross‐correlation methods (Hodgson ) or non‐linear inversion (Rickett et al . ), and applied on a trace‐by‐trace basis. Time‐shifts measured at the top of the reservoir level are attributed to the entire overburden, whilst reservoir‐related time‐shifts are estimated or measured as those accrued only over the reservoir interval.…”

Section: The Magnitude Of 4d Seismic Time‐shiftsmentioning

confidence: 99%

“…The time‐shifts are evaluated using the NLI algorithm of Rickett et al . () ‐ from Ji (). (b) Strong slow‐down and pressure build‐up in a reservoir from gas injection and speedup in the overburden due to compression.…”

Section: The Magnitude Of 4d Seismic Time‐shiftsmentioning

confidence: 99%

See 2 more Smart Citations

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

MacBeth

Mangriotis

Amini

2018

Geophysical Prospecting

View full text Add to dashboard Cite

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.

show abstract

Section: The Magnitude Of 4d Seismic Time‐shiftsmentioning

confidence: 97%

Section: The Magnitude Of 4d Seismic Time‐shiftsmentioning

confidence: 99%

See 1 more Smart Citation

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

MacBeth

Mangriotis

Amini

2018

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…2) Inversion-based methods: Rickett et al [10] describe a non-linear inversion approach, with the objective function being…”

Section: A Conventional Methodsmentioning

confidence: 99%

Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks

Dramsch¹,

Christensen²,

MacBeth³

et al. 2019

Preprint

View full text Add to dashboard Cite

We present a novel 3D warping technique for the estimation of 4D seismic time-shift. This unsupervised method provides a diffeomorphic 3D time shift field that includes uncertainties, therefore it does not need prior time-shift data to be trained. This results in a widely applicable method in time-lapse seismic data analysis. We explore the generalization of the method to unseen data both in the same geological setting and in a different field, where the generalization error stays constant and within an acceptable range across test cases. We further explore upsampling of the warp field from a smaller network to decrease computational cost and see some deterioration of the warp field quality as a result.

show abstract

“…This is a HTHP reservoir and has dominant geo-mechanical effects in reservoir, overburden and underburden. Time-shift data used in this study (Figure 1) are measured from baseline and monitor using three popular methods, which are: correlated leakage method (CLM) by Whitcombe et al (2010), fast cross correlation (DHF) by Hale (2009) and non-linear inversion (NLI) by Rickett et al (2007). Selected results for the NLI are presented in Figure 2.…”

Section: Application To Field Datamentioning

confidence: 99%

Estimation of Time-lapse Velocity Changes Using Gaussian Reconstruction

Nguyen¹,

MacBeth²,

Mangriotis³

2017

Proceedings

View full text Add to dashboard Cite

A key task in 4D seismic analysis is to resolve changes in subsurface velocity ΔV/V that affect both imaging in the monitor data and our interpretation of time-lapse amplitudes. This study introduces a new approach to recover ΔV/V using a Gaussian mixture model. The Gaussians are found to be better representative of the property fields than other choices such as B-splines. This approach is tested by application to a North Sea field, where geomechanical effects are active. Recovery of ΔV/V from three different time-shift estimates, using three approaches is firstly compared with Gaussian reconstruction. A second comparison estimates ΔV/V directly from the trace data. In these tests, the new approach compares favourably in the presence of noise, and is relatively simple to implement.

show abstract

4D Time Strain and the Seismic Signature of Geomechanical Compaction at Genesis

Cited by 11 publications

References 0 publications

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

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

Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks

Estimation of Time-lapse Velocity Changes Using Gaussian Reconstruction

Contact Info

Product

Resources

About