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Record W2011369319 · doi:10.2118/2006-193

Shale Gas Production Potential and Technical Challenges in Western Canada

2006· article· en· W2011369319 on OpenAlexaboutno aff
J Shaw, M. M. Reynolds, Lyle H. Burke

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsShale gasProduction (economics)Environmental scienceOil shalePetroleum engineeringNatural resource economicsMining engineeringGeologyWaste managementEngineeringEconomics

Abstract

fetched live from OpenAlex

Abstract Fractured gas shale reservoirs in the Western Canadian Sedimentary Basin (WCSB) are often overlooked as a result of low initial production rates relating to vertical wellbores that have failed to intersect common vertical fracture sets. A recent study of the shale gas potential in WCSB has indicated there is a large resource potential for shale gas, with greater than 86 tcf of gas in place within Devonian through Cretaceous age shale formations. Furthermore, there is emerging evidence to suggest that shales have been contributing to conventional production. This paper will describe the major challenges of developing a commercial scale shale gas project including:Identifying geologic sweet spots, which meet the following criteria:Organic richThermally matureMarine to transitional marineComparatively thick, and most importantlyPermeability enhanced by fracturing or higherpermeability interbedded faciesAcquiring a large lease base capable of placing large numbers of wells,Designing and field-testing cost-effective drilling, completion, and stimulation procedures that are suited for the specific type of shale gas resources which are essential to the economic success of the project, andContinuous technology improvement and innovation including the use of state-of-the-art fracturing technologies such as micro-seismic to improve well productivity. Examples of a Canadian shale gas field pilot will be discussed to illustrate these key steps, which are critical to developing a successful commercial project. Introduction Fractured shale reservoirs include some of the oldest known oil and gas production in North America. Natural gas was produced from the Devonian shales near Fredonia, NY as early as 1821, which pre-dates any known oil production by almost 40 years. By 1926, the Devonian Ohio shale of the Appalachian basin was in commercial production and was the largest known gas field in the world (1). This area currently contains the majority of shale gas wells in the US, with over 30,000 gas wells producing in the order of 120 bcf/year (2). Fractured shales are differentiated from conventional reservoirs in that they are often both the source rock of the oil and gas, as well as the reservoir itself. The majority of the hydrocarbons are stored within the low permeability matrix rock of the shale reservoir, and a system of natural fractures provides the bulk of the transmissibility (permeability) within the reservoir. The size, extent, interconnectivity, and degree of cementation of the natural fractures typically dominate the overall productivity of the shale reservoir (3). Gas shale reservoirs are classified as continuous type natural gas plays: that is accumulations that are pervasive throughout a large geographic area that offer long-life reserves with attractive finding and development costs (4). Most shale reservoirs have very low matrix permeability (nano to microdarcy level) and require the presence of the extensive natural fracture systems to provide and sustain commercial gas production rates. Natural gas is stored in three ways: as free gas within the rock pores, adsorbed gas on the organic material, and as free gas within the system of natural fractures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.441

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)0.0000.000

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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