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Record W2052509374 · doi:10.2523/iptc-13347-ms

Water Sustainability and the Petroleum Industry

2009· article· en· W2052509374 on OpenAlexaff
Samer Adham, Joel Minier-Matar

Bibliographic record

VenueAll Days · 2009
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsDispose patternSustainabilityBusinessPetroleum industryDownstream (manufacturing)PetroleumUpstream (networking)Water useAgricultureLicenseNatural resource economicsOil refineryEnvironmental scienceWaste managementEngineeringEnvironmental engineeringComputer scienceMarketingEconomics

Abstract

fetched live from OpenAlex

Abstract Water is becoming an increasingly important issue in the world and particularly for the petroleum industry. The purpose of this paper is to outline some of the challenges before the petroleum industry and their need to play a key role to ensure water sustainability. As an oil and gas industry, we are both producers and consumers of water. In the future, it may become an important license—to—operate issue. On the upstream side, we generally produce more water than oil (worldwide about three times more). Historically it has been a nuisance by-product that we pay to dispose of. With environmental regulations tightening and as we pursue more Enhance Oil Recovery (EOR) projects, water supply and quality cannot be overlooked. On the downstream side of the business, we are primarily a user of water — both as process water in the desalter units and as cooling water. In some communities we do business at, we are already starting to compete with the domestic and agriculture users of water. Therefore, we must focus on water much more than we have in the past. ConocoPhillips Global Water Sustainability Center in Doha (Qatar) will coordinate our effort to develop and evaluate innovative solutions. Any solutions that benefit ConocoPhillips (COP) will also benefit Qatar Petroleum (QP) and the industry in general. Introduction Two third of our planet is mainly water and 97.5% of it is salty (sea water); only 2.5% is fresh water and not all of it can be used for direct human uses. From the 2.5% of fresh water, approximately 70% is frozen as glaciers or polar ice; leaving only less than 1% of the total volume of fresh water accessible for human uses (e.g. rivers, lakes and groundwater) and most of the times, water sources are not clean enough.(1) Moreover, the availability of fresh water in the world is not evenly distributed. In places like deserts, the rainfall is very low and the water sources are scarce, so new water treatment strategies as well as water conservation programs are required. Water uses can be classified in three main categories: agriculture, industrial and domestic. Around the world, on average, 8% of water is used for domestic, 22% is used for industrial applications, and 70% is used for agriculture. However, the developed world uses more water for industries than for agriculture as shown in Figure 1. (2)

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 score0.068
Threshold uncertainty score0.172

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 head0.255
Teacher spread0.245 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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