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Record W2008348759 · doi:10.2118/2006-168

Optimized CO2 Miscible Hydrocarbon Fracturing Fluids

2006· article· en· W2008348759 on OpenAlexaboutno aff
Robert S. Taylor, Robert Lestz, D. Loree, Gary P. Funkhouser, Glen Fyten, Doug Attaway, Hannah Watkins

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHydrocarbonPetroleum engineeringMaterials scienceChemical engineeringGeologyChemistryEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract CO2 miscible hydrocarbon fracturing fluids have proven to be a very effective gas well stimulation tool in Canada and the United States. A key reason for their success has been that they circumvent phase trapping associated with aqueous fluids and achieve rapid fracturing fluid recovery through a methane-drive mechanism. This technology is taking on new economic significance because of increased unconventional gas development and sustained higher energy prices. Reservoirs including tight gas, shale gas, and coalbed methane are becoming a critically important component of current and future gas supply. These reservoirs often present unique stimulation challenges. The use of water-based fracturing fluids in low-permeability reservoirs may result in loss of effective fracture half-length caused by phase-trapping effects associated with the retention of a large portion of the introduced water-based fluid in the formation. SPE 75666,1 presented in 2002, described the theoretical basis of CO2 miscible hydrocarbon fracturing fluids in this technology and provided a step-by-step application process. The paper was based on work published by Gruber and Anderson in CIM 95-45. 2 Improved methods of application have been arrived at through field application of this technology in conjunction with additional lab studies. The objective of this paper is to present the findings in the form of one optimized system applicable to all gas-well stimulation applications. Supporting data including viscosity curves and friction data based on actual ISIP measurements is included. The paper also discusses selection of tubulars and flowback procedures in detail. It should be possible to successfully apply the technology to low permeability gas reservoirs through application of the methods described in this paper. This could potentially include some shale gas developments in the future, an area of rapidly growing focus in Canada. Introduction Unconventional gas reservoirs including tight gas, shale gas, and coalbed methane are becoming a critically important component 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 frac 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 several thousand psi can be present in low-permeability formations at low watersaturation 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.

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 score0.317
Threshold uncertainty score0.960

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 science0.0010.000
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 head0.007
GPT teacher head0.200
Teacher spread0.193 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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