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Record W2122647721 · doi:10.2118/09-06-26-tn

Treatment of Oil-Based Drilling Waste Using Supercritical Carbon Dioxide

2009· article· en· W2122647721 on OpenAlexaff
Christianne Street, Selma E. Guigard

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDrilling fluidDrill cuttingsPetroleum engineeringExtraction (chemistry)Supercritical fluidDrillingLubricitySupercritical fluid extractionResidual oilWaste managementDrillEnvironmental scienceMaterials scienceEngineeringChemistryChromatographyMetallurgy

Abstract

fetched live from OpenAlex

Abstract Oil-based drilling fluids are essential for challenging drilling operations. However, their use requires costly handling, treatment and disposal. Supercritical fluid extraction is herein 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 the base oil from drilling waste are presented. Current work investigates the extraction of hydrocarbons (i.e. base oil) from a synthetic oil-based centrifuge underflow drilling waste. Extraction efficiencies as high as 98% have been observed. Additionally, results of both past and current studies indicate that the hydrocarbons are unchanged by the extraction process and that they may be recovered and potentially reused. Introduction In rotary drilling, drilling fluids are essential to lubricate the drill bit and circulate the drill cuttings to the surface. It is well documented that oil-based drilling muds (OBMs, sometimes referred to as non-aqueous drilling fluids or NADFs) have several advantages over their water-based counterparts(1, 2). OBMs have a higher natural lubricity, making them suitable for challenging drilling operations. Also, OBMs are less reactive with clays and shales, thereby preventing hole enlargement, resulting in smaller overall waste volumes. However, OBMs must be carefully handled and treated prior to disposal due to their potential negative environmental effects. There are a number of options available to treat and dispose of OBM drilling wastes (for example, land spreading and landfilling), 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 for favourable mass transfer of soluble waste components (i.e. hydrocarbons) from solid matrices 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 OBM 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 an application of the technology, and indicates 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 score0.137
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
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 head0.008
GPT teacher head0.197
Teacher spread0.189 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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