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Record W2195991702 · doi:10.1190/geo2014-0489.1

Double-difference waveform inversion: Feasibility and robustness study with pressure data

2015· article· en· W2195991702 on OpenAlexaff
Di Yang, Mark A. Meadows, Phil Inderwiesen, Jorge L. Landa, Alison Malcolm, Michael Fehler

Bibliographic record

VenueGeophysics · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInversion (geology)WaveformRobustness (evolution)GeologySeismic inversionSynthetic dataSeismic surveyComputer scienceAlgorithmSeismologyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Time-lapse seismic data are widely used to monitor reservoir changes. Qualitative comparisons between baseline and monitor data sets or image volumes provide information about fluid and pressure effects within the reservoir during production. However, to perform real quantitative analysis of such reservoir changes, quantitative estimates of the elastic parameters are required as input parameters to rock-physics-based reservoir models. Full-waveform inversion has been proposed as a potential tool for retrieving subsurface properties, such as P- and S-wave velocities and density by fitting simulated waveforms to seismic data. An extension of this method to time-lapse applications seems straightforward, but, in fact, it requires more tailored processes such as double-difference waveform inversion (DDWI). We used realistic 2D synthetic pressure data examples to compare the performance of DDWI with that of two other inversion schemes: one using the same starting model for both inversions and the other starting the monitor inversion with the final baseline inversion model. The data simulation and inversion were based on acoustic theory. Although P-wave velocity changes were reliably recovered by each inversion method, DDWI was found to deliver the best results when perfectly repeated surveys were used. However, differencing the baseline and monitor data sets, as required by DDWI, could be found to be sensitive to the presence of survey nonrepeatability. To investigate the feasibility of using DDWI in practice, the dependence of DDWI on the quality of the baseline models and its robustness to survey nonrepeatability were studied with numerical tests. Various types of nonrepeatability were considered separately in the synthetic tests, including random noise, acquisition geometry mismatch, source wavelet discrepancy, and overburden velocity changes. A study of the correlation between the levels and types of nonrepeatability and the resulting contamination of the inversion results found that, for pressure data, DDWI was capable of inverting reliably for P-wave velocity changes under realistic survey nonrepeatability conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.089
GPT teacher head0.261
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations40
Published2015
Admission routes1
Has abstractyes

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