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Record W2086727806 · doi:10.2118/2000-024

Use of a Numerical Petroleum Reservoir Simulator to Model Aspects of Environmental Contamination and Remediation Processes

2000· article· en· W2086727806 on OpenAlexaff
M. Aikman, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEnvironmental remediationContaminationPetroleumPetroleum engineeringEnvironmental scienceReservoir simulationComputer scienceGeologyEcology

Abstract

fetched live from OpenAlex

Abstract The use of numerical simulation to assess the productive response of petroleum reservoirs under various development operations is a powerful and valuable tool for the economic exploitation of oil and gas bearing reservoirs. The computational power and software coding has advanced to such a level that nowadays, that it is imprudent to develop most petroleum reservoirs without some use of numerical simulation to examine the optimality of various development opportunities. In this paper, we examine the application of numerical simulation, using Eclipse-100(1), to model the processes of contamination of soil by a light hydrocarbon, and also the subsequent remediation of the contamination using water and air displacement. Some insight into what sort of data acquisition program is required to model the likely contaminant plume is provided. A better understanding of the process of contamination under various geologic scenarios, and how the remediation operation must be tailored to the situation, is demonstrated. Last, some thoughts on extending such numerical simulation models to better suit some of the key mechanisms of remediation are listed. Introduction Fiscal Justification In our modern world, one of the consequences of industrial development is an impact on our surrounding environment. Such an impact is pollution and contaminant release of some form. Consider only the production, refining, and distribution of gasoline and kerosene fuel products. Pollution occurs at the well head, or even in the well bore as the fluid is produced from the petroleum reservoir. And as the crude is transported from the well head, to the processing facility, to the refinery tank farm, through the distillation and refining equipment, and on to the service station for distribution to the consumer, pollution is always a possibility. If the refined product at the service station is stored in a carbon steel underground storage tank ("UST") that has corroded and therefore is leaking, a large volume of fluid can be released over time. Indeed, before 1982, the underground storage tanks were not even designed with anticorrosion methods(2). So decades of exposure can result is very large release volumes. The hydrocarbon spill would migrate down into the soil. It could then spread out, and impact a large area, and impinge on the other surrounding infrastructures, such as housing or school buildings. It is estimated(2) that in the United States alone, there are over 1.6 million underground storage tanks in use or retired from use. It is further estimated that about 20% of these are releasing (or have released) some of their contents into the soil. The clean up cost for an individual site can range from $10 thousand to $125 thousand (in terms of 1994 U.S. dollars). It is more likely that the cost will be on the high end of the estimate as more complex sites are treated, and as regulatory constraints become more stringent. An average cost can be derived from a detailed probability distribution.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2000
Admission routes1
Has abstractyes

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