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Record W2077755446 · doi:10.2118/2006-118-ea

Treatment of Non-Aqueous Drilling Waste Using Supercritical Carbon Dioxide

2006· article· en· W2077755446 on OpenAlexaff
Christianne Street, Selma E. Guigard

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSupercritical carbon dioxideCarbon dioxideAqueous solutionWaste managementSupercritical fluidSupercritical water oxidationEnvironmental sciencePetroleum engineeringChemistryGeologyEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Non-aqueous drilling fluids, both synthetic and oil based, are essential in challenging drill operations. However, their use requires costly handling, treatment and disposal. Supercritical fluid extraction is investigated as a novel technology to treat this waste. Supercritical fluid extraction employs a substance above its critical temperature and pressure as a solvent; in this state the substance has both liquid- and gas-like properties that can be controlled by the pressure and temperature of the extraction process. In this paper, results of studies using supercritical carbon dioxide to remove non-aqueous fluids from drilling waste are presented. Current work investigates the extraction of hydrocarbons from centrifuge underflow synthetic based drilling waste. Although the extraction process is not yet fully optimized, extraction efficiencies as high as 98% have been observed. Additionally, results of both past and current studies indicate that non-aqueous drilling fluids are unchanged by extraction process and that the recovered hydrocarbons may potentially be collected and reused. Introduction In drilling for oil and gas, drilling fluids are essential to lubricate the drill bit and circulate the drill cuttings to the surface. It is well documented that non-aqueous drilling fluids (NADFs) have several advantages over water based drilling Fluids(1,2). NADFs have a higher natural lubricity, making them suitable for challenging drilling operations. NADFs are less reactive with clays and shales, thereby preventing hole enlargement and producing smaller volumes of waste overall. However, NADFs are more expensive and the waste generated from their use must be carefully handled and treated prior to disposal. There are a number of options available to treat and dispose of NADF wastes (land spreading and landfilling for example), however the cost of handling and disposing of drilling waste is increasing. The drilling industry is now turning to novel approaches for the treatment and disposal of drilling wastes in order to meet more stringent environmental guidelines(3). Supercritical fluid extraction (SFE) is an extraction technique that uses substances at or above their critical pressure and temperature as solvents. In the vicinity of the critical point, the liquid and vapour phases of the substance merge producing a fluid with gas-like diffusivity and viscosity and liquid-like density(4,5). These properties provide favorable mass transfer of the NADF hydrocarbons from the drilling waste to the bulk supercritical fluid. The density of the fluid is defined by the pressure and temperature; small changes in processing conditions can fine-tune the solvating power of the fluid(6). Additionally, supercritical fluids have zero surface tension thereby allowing easy penetration into most matrices(4). Several studies have documented the treatment of NADF drilling waste using SFE. In 1984, a patent by Eppig et al. detailed a system suitable for the removal of organic contaminants from inorganic matrices(7).This patent specifically lists the treatment of oil contaminated drill cuttings as a suitable application of the technology, and that propane, Freon and carbon dioxide would be suitable supercritical fluids for this purpose. A later study by Eldridge investigated the use of a pilot-scale SFE system to treat oil contaminated drill cuttings from North Sea drilling platforms(8).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.994

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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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