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Record W2083779769 · doi:10.4043/17454-ms

Colored Inversion: Application In A Tertiary Basin Offshore China

2005· article· en· W2083779769 on OpenAlexaff
August Lau, Junhu Dai, A. J. Robinson, Ben Flack, Chung‐Chi Shih, Randy Utech, Nikhil Banik

Bibliographic record

VenueOffshore Technology Conference · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsSubmarine pipelineColoredInversion (geology)GeologyStructural basinChinaComputer scienceOceanographyPaleontologyGeographyMaterials scienceArchaeology

Abstract

fetched live from OpenAlex

In a Tertiary basin offshore China, we used "Colored Inversion" (Lancaster and Whitcombe, 2000) to study the distribution of sand and shale in the subsurface. The geological complexity and the consequent borehole and seismic data inconsistencies in the area necessitated the application of colored inversion instead of other, more preferred, inversion methods. Colored inversion was used in a multi-attribute hybrid-inversion workflow to generate relative acoustic and shear impedances and density volumes so reservoir properties could be determined through stratigraphic interpretation. We discuss the results and the lessons we have learned. Introduction Lancaster and Whitcombe (2000) presented colored inversion (CI) in a heuristic approach for inversion of seismic data into relative impedances. In CI, a single operator convolves with the seismic traces to produce relative impedances. The CI operator has amplitude spectrum that maps the mean seismic spectrum to the mean log-impedance spectrum and has a constant phase of -90°. The impedance logs are incorporated in the creation of the CI operator based on the assumption that the gross spectral form of impedance logs from wells in any given field is constant. Although algorithmically crude, the method has the appeal of simplicity and quickness of application. No explicit wavelet extraction or spectral shaping, which are often very complex processes, is needed. We found the method useful in the offshore China study area. The basin consists of a Tertiary fluvial-lacustrian system draped over Ordovician horst blocks. The depositional system is modeled as an interconnected series of channels, levies, and point bars. Many thin gas- and oil-bearing sand zones constitute the shallow reservoirs. The primary inversion-study objective was to delineate these sands and find their interconnectivities, starting very shallow at the upper Tertiary, to the top Ordovician unconformity. Although several exploratory wells and some developmental wells exist in the area, the quality of the logs was poor--the log-responses were not consistent with each other, and or with the corresponding seismic data. The wavelets extracted through well ties varied significantly from location to location. Also, the quality of seismic data was inappropriate for a model-based inversion because of the presence of numerous normal faults associated with the horst blocks. Under the circumstances, we concluded that inverting for relative impedances and density rather than for their absolute values, and using a simplified, unconstrained sparse-spike inversion (Oldenburg et al., 1983) would be preferred. We used CI, which was known to work significantly better than the conventional recursive inversion and benchmarked well against the unconstrained sparse-spike inversion (Lancaster and Whitcombe, 2000). The application provided acceptable relative acoustic and shear impedances for stratigraphic interpretation, delineating reservoir sands in the area. We also carried out a detailed and integrated reservoir description (IRDTM) based wavelet processing and spectral shaping process described by Poggiagliomi and Allred (1994) on the Kirchhoff Prestack Time Migration (PSTM) stack and compared the results with those of CI.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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.

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

Citations4
Published2005
Admission routes1
Has abstractyes

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