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Record W1975507421 · doi:10.2118/05-08-wpc3

Water Resources Management and the Energy Industry in Alberta, Canada

2005· article· en· W1975507421 on OpenAlexaboutno aff
Sheldon Gordon, H.C.F. Kooistra Wiebe, R. Jacksteit, S. Bennett

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueNatural resourceNatural resource economicsPetroleum industryPopulationBusinessFossil fuelResource (disambiguation)Water resourcesAgricultural economicsEnvironmental protectionEnvironmental scienceEngineeringEnvironmental engineeringEconomicsWaste management

Abstract

fetched live from OpenAlex

Abstract The challenges associated with managing Alberta's water resources are increasing as a result of population growth, agricultural expansion, and industrial development, including the energy industry. This challenge is being addressed through creative initiatives at the provincial and local levels, and reflects the involvement of many stakeholders. The road ahead will involve: continued development of stakeholder-driven water management initiatives; ongoing refinement and clarification of water management objectives; and, an improved understanding of the aquifer system, including its connection to surface water. Water is an essential resource for Alberta's energy industries, which produce 70% of Canada's crude oil and 80% of its natural gas, and these industries have done much to minimize the use of water. Nevertheless, the challenge to the energy sector will be continued development of water conservation and water quality management technologies. Introduction Energy industries are of significant importance to Albertans. Alberta is rich in natural gas, oil sands, and conventional oil, and produces 70% of Canada's crude oil and 80% of its natural gas(1). Energy-related royalty revenues account for about one-third of total provincial revenue (about $7.7 billion in 2003 - 2004)(2). The mining and oil and gas industries account for more than 17% of provincial GDP. For comparison, crop and animal production contributes 1.6% to provincial GDP(3). Conventional oil, natural gas, and coal are found in many parts of the province, and heavy oil and oil sands are found in the north and east (Figure 1). The energy industry requires water for many aspects of production, in particular for enhanced oil recovery (oilfield injection), in situ heavy oil recovery, oil sands mining, and dewatering for coal bed methane. Alberta is also experiencing high population and economic growth in other sectors, also leading to increasing demands on water resources, especially in the central and southern parts of the province. Currently, there is a moratorium on surface water withdrawals for some streams in Southern Alberta. In contrast, surface water is most available in the northern part of the province. Ground water availability is less known across the province, and this lack of information creates uncertainty in the role of ground water for supply and ecosystem health. The province, therefore, faces numerous and unique water management challenges related to the energy sector and to growing demand, fluctuations and limitations in supply, the need to maintain healthy aquatic ecosystems, and commitments embedded in downstream water agreements with adjacent neighbours(4). This paper describes the issues and challenges for water management in Alberta related to the energy sector in particular. It also highlights the current policy and management strategies developed by stakeholders to address these challenges in a practical and often creative manner, to ensure sustainability of water resources while satisfying future growth opportunities. Alberta's Water Resources Alberta is one of Canada's western prairie provinces and covers 661,185 km2. The prairies are one of the driest regions of Canada. Alberta has a diversity of hydrological and hydrogeological settings that are characterized by varied physiography, geology, and climate.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.560

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.003
GPT teacher head0.156
Teacher spread0.154 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2005
Admission routes1
Has abstractyes

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