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Record W1971949165 · doi:10.2118/0512-0076-jpt

Assessing the Magnitude and Consequences of Reservoir Souring

2012· article· en· W1971949165 on OpenAlexaboutno aff
M. Schofield, Jim Stott

Bibliographic record

VenueJournal of Petroleum Technology · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsnot available
Fundersnot available
KeywordsSour gasStress corrosion crackingCorrosionHydrogen sulfideEnvironmental sciencePetroleum engineeringCrackingPetroleumWaste managementPetroleum industryForensic engineeringNatural gasEngineeringGeologyEnvironmental engineeringChemistryMaterials scienceMetallurgySulfur

Abstract

fetched live from OpenAlex

Reservoir souring Reservoir souring, defined as the unplanned increase in hydrogen sulfide (H2S) in produced fluids during field life, is a growing concern for the petroleum production industry. H2S is a poisonous, dense gas with serious safety implications; it can lead to sudden catastrophic failure of nonresistant metallic materials from sulfide stress corrosion cracking or hydrogen-induced cracking, and it can enhance pitting corrosion rates. But how does reservoir souring develop, and how can its consequences for materials be anticipated? Consideration also needs to be given to the currently available options for souring control and their shortcomings. Despite the significant advances that have been made over the past 30 years or so in the understanding of H2S- related materials degradation mechanisms, H2S still causes many failures every year. While it may be understandable that such failures continue to occur in operating environments where reservoir souring is a new phenomenon, failures are also occurring where the industry is mature. For example, recently in the UK, a carbon steel pipeline transporting sour hydrocarbons onshore failed by sulfide stress cracking (SSC) after 6 weeks of operation, necessitating replacement at a cost of GBP 100 million. Over 2000–01, sour gas pipelines failures, including those affected by both microbial and chemically induced corrosion, accounted for 35 (4%) of the 952 pipeline failures in Alberta, Canada. The challenge of dealing with H2S is likely to rise in importance as an increasing number of high-temperature, high-pressure (HTHP) reservoirs are exploited in the future. This will require more widespread use of corrosion resistant alloys (CRAs), increasing the costs of wells and downstream equipment. Therefore, we need more corrosion data on a range of alloys when there are fewer materials engineers (particularly metallurgists) coming into the industry and fewer corrosion testing facilities in steel companies, meaning that operating companies and even fabrication contractors are having to take on the task of generating materials susceptibility data. H2S Generation and Mobility Reservoir souring is the production of increased concentrations of H2S in well-stream fluids from production wells subject to water injection for secondary recovery. It is generally acknowledged to be caused by the activity of a specialized group of microorganisms, the sulfate-reducing bacteria (SRB). Low populations of SRB cells are ubiquitous in seawater and many other natural waters that are used for secondary recovery. Biogenic H2S originates solely from SRB activity in the water phase and subsequently partitions between water, liquid hydrocarbon, and gas, dependent on temperature, pressure, the pH of the aqueous phase, fluid phase ratios, and a number of other factors. However, the progress of reservoir souring is routinely measured and expressed in terms of H2S concentrations in the gas phase at separator conditions and the corresponding partial pressures of gaseous H2S.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.324
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2012
Admission routes1
Has abstractyes

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