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Record W2034721584 · doi:10.2118/2009-153

Dominant Considerations for Effective Hydraulic Fracturing in Naturally Fractured Tight Gas Carbonates

2009· article· en· W2034721584 on OpenAlexaffabout
James Arukhe, Roberto Aguilera, Thomas G. Harding

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTight gasHydraulic fracturingPetroleum engineeringGeologyFracturing fluidUnconventional oilOil shale

Abstract

fetched live from OpenAlex

Abstract This study examines the results of laboratory work to establish rock strength data, acid solubility, fracture fluid selection and mineral identification of a fractured tight gas carbonate reservoir. Basic to a successful acid fracture design are acid etching and rotating disc tests which show for a given acid system, conductivity at a given stress (etched width or how much rock is eaten away) and parameters necessary to determine acid reaction rate, reaction order, rate constant and energy of activation at a given temperature. These tests address the measurement of mass transfer and diffusion with or without leak off in carbonates, and also enable the prediction of reactivity versus temperature for various acid strengths. Dynamic fluid losses are measured experimentally and laboratory data are converted to an estimate of in-situ leak off. The leak off profile and wall building coefficients enable a consideration of fluid loss additives for fracturing fluids to build up pressure for fracture opening. In the fracture conductivity tests, closure stress is applied across a test unit for sufficient time to allow the proppant bed to reach a semi-steady state condition while test fluid is forced through the bed. At each stress level, pack width, differential pressure, and average flow rates are measured as fluid is forced through the proppant bed. The proppant pack permeability and conductivity are then evaluated and compared. Introduction A discussion of dominant considerations for effective hydraulic fracturing in naturally fractured tight gas carbonates is presented along with the results of laboratory work to establish rock mechanical properties data, acid solubility, fracture fluid selection and mineral identification for a selected naturally fractured tight gas carbonate reservoir.1 The carbonates under consideration are located in the Western Canadian Sedimentary Basin (WCSB) in what is usually known as the "Deep Basin" of Alberta (Figure 1). The core samples studied come from the Savannah Creek field (Figures 2) and correspond to the Rundle group Mississippian Mount Head and Livingston carbonates (Figure 3). These carbonates were deposited in a shallow marine ramp setting. These are upward-shallowing cycles ranging from crinoid / bryozoan shoals to lagoonal mud facies. The reservoirs comprise dolomudstones and wackstones with an average pay of approximately 35 m. Reservoir zones can be discontinuous due to lateral facies changes and minor faulting. The presence of natural fractures in the tight formations considered in this research is corroborated by cores and thin sections. Notice the presence of calcite cemented fractures in the whole core and plugs displayed in Figure 4. The thin section shown on Figure 5 presents calcite-filled fractures (pink strip running from upper left to lower right) that have been re-fractured (thin blue streak). The thin section work corroborates that it possible to re-fracture existing healed fractures. General Considerations There are many mechanisms that contribute to the final created geometry (fracture height, fracture width, hydraulic or created fracture length or effective fracture length)2–8 and its evolution in naturally fractured tight gas carbonates. Pump rate, volume injected, fluid viscosity, fluid loss and proppant scheduling combine with static and dynamic rock properties.

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 score0.140
Threshold uncertainty score0.996

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 communication0.0000.000
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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

Citations5
Published2009
Admission routes2
Has abstractyes

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