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Record W2004008691 · doi:10.2118/2007-112

Enhanced Aqueous Fracturing Fluid Recovery From Tight Gas Formations: Foamed CO2 Pre-Pad Fracturing Fluid and More Effective Surfactant Systems

2007· article· en· W2004008691 on OpenAlexaboutno aff
H.C. Tamayo, K.J. Lee, Robert S. Taylor

Bibliographic record

VenueCanadian International Petroleum Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFracturing fluidPetroleum engineeringPulmonary surfactantAqueous solutionTight gasEnhanced oil recoveryHydraulic fracturingMaterials scienceChemical engineeringGeologyChemistryEngineering

Abstract

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Abstract Underpressured, tight, deep formations represent a challenge in terms of recovery of fracturing fluids. CO2, N2, and binary high-quality foams are widely used in this type of reservoir due to their capacity to energize the fluid and improve total flowback volume and rate. Surfactants designed to reduce surface and interfacial tension are also a key element in the design of fluid systems to enhance recovery and reduce entrapment of fluid barriers within the formation. Enhanced fluid recovery improves overall completions economics due to less total treatment cost and less time required for flowing back fluids. The most important benefit is achieving a less damaged proppant pack, resulting in higher fracture conductivity. This document will discuss the application of CO2 foamed fluids and surfactants to enhance fracturing fluid recovery and other techniques adopted by one operator in the Wild River field to improve completion practices. Introduction Unconventional gas in place in Canada (not including gas hydrates) has been estimated at approximately 2,589 Tcf; 1,500 Tcf of these reserves correspond to tight gas.[1] Achieving optimum development of these reserves is challenging due to the low permeability (<0.1 mD) and abnormal pressures that characterize tight gas sands. The challenges are both technical and economic. Unconventional reservoirs in general require higher capital expenditure compared to conventional reservoirs.[2] Commercial rates are in most cases achieved by hydraulically fracturing pay zones. The perfect fracturing job in these cases would consist of an inexpensive, long fracture with infinite conductivity, 100% propped, 100% effective length, and contained in the pay zone with 100% fluid recovery. However, realistically we know that a tight gas-bearing zone faces abnormal pressure, low permeability, clay swelling and migration, capillarity effects, near-wellbore restrictions, and formation complexity and heterogeneities. These characteristics usually result in damage from drilling and cementing operations, water phase trapping, screenouts, proppant not carried to the far field, dehydrated polymer, etc. This paper will review different practices that are proving to mitigate some of these problems, focusing on enhanced fracturing-fluid recovery with the aid of high-quality CO2 foamed fluid as a pre-pad and the addition of solvents/surfactants. The experience gained after performing 192 fracturing jobs in the Cadomin formation, Wild River field, central Alberta will help illustrate the benefits of the practices recommended in this document. Description: Cadomin Formation, Wild River Field The practices described in this document are based on the experience acquired fracturing in the Cadomin formation of the Wild River field in Alberta, Canada (Figure 1). Cadomin formation is a deep-basin, tight gas formation with NW-SE trending, usually the lowest reservoir in sequence, and consequently, highly stressed. The Cadomin formation is a braided channel deposit, sourced by conglomeratic and sandstone alluvial fans to the west. This zone is present in every Wild River well.[3] The common reservoir parameters of the Cadomin formation are presented in Table 1. A formation that is underpressured, highly stressed, and has a rock matrix with sandstones and conglomerates represents a challenge in terms of proppant placement and fluid recovery.

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 categoriesMeta-epidemiology (narrow)
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 score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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 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

Citations8
Published2007
Admission routes1
Has abstractyes

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