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Record W2027582046 · doi:10.2118/06-08-tn1

Changes in Porosity Due to Acid Gas Injection As Determined by X-Ray Computed Tomography

2006· article· en· W2027582046 on OpenAlexafffund
M.A. Vickerd, Ronald W. Thring, J. M. Arocena, Jianbing Li, Richard J. Heck

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of GuelphUniversity of Northern British Columbia
FundersUniversity of British ColumbiaBaker HughesUniversity of GuelphDuke Energy
KeywordsPorosityDissolutionDispose patternNatural gasTRACERSaturation (graph theory)Effective porosityMineralogyPorous mediumPetroleum engineeringMaterials scienceEnvironmental scienceChemical engineeringWaste managementGeologyGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract There is an increasing trend to dispose acid gases (H2S and CO2) generated from natural gas processing by geologic sequestration. Industry and government must carefully assess the compatibility of the formation matrix and fluids, with the intended injection stream, to ensure safe and efficient facility operations. We investigated cores subjected to acid gasflooding using X-Ray computed tomography (CT) analysis. The changes in pore morphology, pore size, and pore distribution were examined. Results indicated that the porosity roughly doubled and pore size increased substantially. Visually distinct areas of the core suggested that changes in porosity were non-uniform, probably due to the natural heterogeneity in the rock and/or regions of nonuniform flooding due to lack of pore interconnectivity in the matrix. Detailed CT image analysis revealed traces of halite (NaCl) probably due to the desiccation of the initial water saturation by the highly under-saturated injected gas. Our results suggest that changes in porosity are not attributed to dissolution processes but due to fine dislodgement and desiccation. Consequently, the permeability and porosity increases were the result of physical processes rather than chemical ones. Introduction Acid gas injection into geologic formations is an alternative way to dispose the undesirable waste stream produced from natural gas processing. This technology allows sour gas disposal to be economically viable and also provides a more environmentally friendly option compared to the conventional flaring method. To ensure successful disposal in a safe and efficient manner, adequate disposal information such as the compatibility between the fluid and the reservoir matrix is needed. It is a regulatory requirement to prove the formation and injection fluid compatibility as stated in government disposal guidelines(1) The rock permeability and porosity are the most important properties that control the fluid flow necessary for a successful injection operation, and they are typically assessed through core sensitivity tests(2–4). Alteration of the formation porosity or permeability during acid gas injection occurs mainly through chemical or mechanical mechanisms(3) such as desiccation, dissolution, and fine dislodgement. Usually these mechanisms are assumed to impose detrimental effects on the permeability of the matrix, but in some cases, benefits result from injection operations due to increases in permeability and/or porosity(2) Desiccation Prior to the actual injection, the acid gas stream, once separated from the natural gas stream, undergoes the processes of compression and dehydration. Dehydration removes water in the gas stream to reduce the possibility of hydrate formation. Once the undersaturated acid gas stream enters the formation via the injection well, desiccation of the formation occurs upon contact with the irreducible water present in the pore spaces(2, 3). Reduction in water saturation increases injectivity due to lessening of adverse permeability effects associated with the presence of the initial water in the porous media(2). A concentrative effect occurs if the initial irreducible water is highly saturated with soluble salts or if the initial water is reduced to significantly low levels such that the remaining water is supersaturated with dissolved ions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.002
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 head0.003
GPT teacher head0.176
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations12
Published2006
Admission routes2
Has abstractyes

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