MétaCan
Menu
Back to cohort
Record W1979320687 · doi:10.2118/59778-ms

Reservoir Souring in the Caroline Field

2000· article· en· W1979320687 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsHydrogen sulfidePetroleum engineeringSeawaterMechanism (biology)Hydrogen sulphideField (mathematics)SulfateSour gasMaterial balanceEnvironmental scienceGeologyChemistryOceanographyEngineeringProcess engineeringWaste managementNatural gasPhysics

Abstract

fetched live from OpenAlex

Abstract This paper presents a novel mechanism for reservoir souring which is based on the evolution of acid gas from sour aqueous phases present in the reservoir. Souring is a widespread phenomenon in seawater floods. The accepted mechanism in these cases is biogenic activity of sulfate reducing bacteria (SRB). Field data from the Caroline reservoir indicate that it is souring. What is intriguing about this field is that it is being developed via conventional blowdown depletion, which suggests that SRB is not the cause. The mechanism presented is based on the physical principles of Henry's Law, which govern the solubility of hydrogen sulfide (H2S) in water. Through material balance analysis and reservoir simulation, the Caroline field is presented as a case study where this mechanism is plausible. Reservoir simulation which account for this phenomenon was subsequently used to generate more realistic gas composition, thus optimizing the operations of the $1 billion Caroline facility.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.833

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)0.0010.000

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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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