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Record W4406585817 · doi:10.1155/gfl/3626427

Characterization and Spatial Distribution of Sand Group Architecture and Channel Types in Tight Gas Reservoirs: A Case Study From the Jurassic Shaximiao Formation of the Jinqiu Gas Field in the Central Sichuan Basin of China

2025· article· en· W4406585817 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeofluids · 2025
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsPetro-Canada
FundersPetroChina Company Limited
KeywordsGeologyNatural gas fieldGroup (periodic table)Spatial distributionTight gasCharacterization (materials science)Channel (broadcasting)GeomorphologyPetrologyGeochemistryPetroleum engineeringNatural gasRemote sensingMaterials scienceChemistryEngineering

Abstract

fetched live from OpenAlex

There is an abundance of tight gas resources in narrow channel sand‐bodies from the Jurassic Shaximiao Formation of the Jinqiu gas field in the central Sichuan Basin of China. The architecture of sand group in the study area is undefined, and the spatial distribution of channel sand‐bodies is unclear. The complex and inhomogeneous sandstones have a significant impact on the reservoir’s physical properties and the fluid mobility of the reservoir. In this study, data from drilling cores, logs, seismic, and experiment testing were used to investigate the spatial distribution of sand group architecture and the channel types. There are five channel genetic types, including the multiphase superimposed type, deeply incised type, abandoned type, progradational superimposed type, and normal single genetic type. Based on the channel genetic types, the ratio of sandstone and mudstone, the ratio of width to depth, the connectivity ratio of sand‐bodies, and the production dynamic characteristics, the channel sand‐body connectivity is defined into three types. The connected sand‐bodies occur in the multiphase superimposed and deeply incised types of channels, with an average connectivity ratio of 83%, a ratio of sandstone and mudstone larger than 0.9, and a ratio of width and depth larger than 40. Based on the association of sandstone and mudstone and rhythmic structure, the sand group architecture can be divided into three types, including (A) uniform‐grain‐sequence pure sandstone architecture, (B) positive‐grain‐sequence thick sandstone and thin mudstone architecture, and (C) positive‐grain‐sequence thick mudstone and thin sandstone architecture. There is a high content of natural gas in Types A and B of sandstones, with a daily gas production of 29.16 × 10 4 –47.6 × 10 4 m 3 /day and pressure coefficients of 0.72–1.08. The sand group architecture of the study area is mainly controlled by the channel sinuosity and the ratio of accommodation and sediment supply, and Types A and B of sand group architectures occur with large channel sinuosity of 1.14–1.36 and a large ratio of accommodation and sediment supply of 0.61–2.92. Based on the connectivity degree of channel sand‐bodies, the sand group architectures, and production data, the channels of the study area can be divided into three types. Type I channels mainly occur in Sand Groups 6, 8, and 9, and Type II and Type III channels occur in Sand Groups 6 and 7 in the western and southern parts of the study area. The technology of fine characterization for channel sand‐bodies on the basis of human–computer interaction and seismic attributes is proposed, and geological modelling of the spatial distribution of sand group architectures and channel types is established. The research results achieve a theoretical breakthrough in the characterization of the sand‐body structure of tight sandstone reservoirs in narrow river channels and assist in the efficient exploration and development of tight sandstone gas.

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.

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 score0.198
Threshold uncertainty score0.536

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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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