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Record W4229650013 · doi:10.2118/2006-081

Total Phosphorus Recovery In Flowback Fluids After Gelled Hydrocarbon Fracturing Fluid Treatments

2006· article· en· W4229650013 on OpenAlexaff
G. Fyten, P. Houle, R.S. Taylor, P.S. Stemler, A. Lemieux

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsFracturing fluidPetroleum engineeringHydrocarbonPhosphorusGeologyEnvironmental scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract CIPC paper 2004–0491 described refinery plugging caused by volatile phosphorus components originating from phosphate ester oil gellants. Also documented were two successful field trials of new phosphonate ester oil gellants shown to address this problem. CIPC paper 2005–0432 presented results of additional fieldtesting of phosphonate ester gellants directed at optimization of cost & performance. A maximum 0.5 ppm volatile phosphorus in crude specification has been proposed to address costly unplanned refinery shutdowns. This specification is based on what is concidered achievable through a combination of new chemistry and typical field dilution. However, this specification is based on average concentrations of phosphorus added to the oil to gel it and assumes the oil is phosphorus free to begin with. In some flowback studies total and resulting volatile phosphorus concentrations greatly in excess of that added have been observed. In addition, refinery plugging is more the result of total phosphorus throughput than peak concentrations at any one point. Therefore, an understanding of total phosphorus recovery in addition to peak concentrations is needed. The objectives of this paper are to study:Total percent recovery of phosphorus originally added as phosphorus based gellant.Total percent recovery of volatile phosphorus as a function of total phosphorus.Peak concentrations of total and volatile phosphorus.Phosphorus concentrations in new and reused fracturing fluids before addition of gellants.Potential explanations for phosphorus concentrations significantly higher than those originally added. Introduction Unconventional gas reservoirs including tight gas, shale gas, and coalbed methane are becoming critically important components of current and future gas supply. These reservoirs often present unique stimulation challenges. The use of waterbased fracturing fluids in low-permeability reservoirs may result in loss of effective fracture half-length caused by phase trapping associated with the retention of the introduced water-based fluid into the formation. This problem is increased by the water-wet nature of most tight gas reservoirs (where no initial liquid hydrocarbon saturation is or ever has been present) because of the strong spreading coefficient of water in such a situation. The retention of this increased water saturation in the pore system can restrict the flow of produced gaseous hydrocarbons such as methane. Capillary pressures of 10–20 MPa or higher per thousand can be present in low-permeability formations at low water-saturation levels. The inability to generate sufficient capillary drawdown force using the natural reservoir drawdown pressure can result in extended fluid-recovery times or permanent loss of effective fracture half-length. Furthermore, use of water in subnormally saturated reservoirs may also reduce permeability and associated gas flow through a permanent increase in water saturation of the reservoir. Secondary costs such as rig time for swabbing can add to the negative economic impact. The effects of fracturing fluid retention on gas flow in the fracture face can be as important a consideration as fracture conductivity when designing a treatment. It is possible to have a conductive fracture with good half-length in the desired productive zone and still not realize economic or optimum gas production if phase trapping and/or relative permeability effects are restricting gas flow.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.999

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 head0.008
GPT teacher head0.235
Teacher spread0.226 · 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.

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
Published2006
Admission routes1
Has abstractyes

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