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Record W2092175005 · doi:10.2118/2006-045

Geological Controls on the Origin of Heavy Oil and Tar Sands and Their Impacts on In Situ Recovery

2006· article· en· W2092175005 on OpenAlexaff
Haiping Huang, Barry Bennett, Thomas B. P. Oldenburg, Jennifer Adams, Steve Larter

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOil sandsIn situGeologytar (computing)Petroleum engineeringEnvironmental scienceChemistryMaterials scienceAsphaltComputer scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Biodegradation of crude oil in subsurface petroleum reservoirs is an important alteration process affecting most of the world's oil deposits. The process preferentially removes light components from conventional oil to form heavy oil and tar sands, which are more difficult to produce and are more costly to refine. Although reservoir temperature is a key control on biodegradation, large variations in oil properties have been documented in accumulations from similar depths within a play area. Data from the Liaohe Basin, NE China and other basins in China and elsewhere, indicate that biodegradation is most active in a narrow zone at or near the base of the oil column in contact with the water leg. The availability of nutrients from mineral dissolution within the water leg is also thought to have a significant impact upon the degree of biodegradation. Thus the level of biodegradation increases with water leg thickness. Charge history and in-reservoir mixing, of continuously charged oil with residual biodegraded oil also have a significant impact on oil physical properties. The conceptual biodegradation model proposed combines geochemical and geological factors to provide a coherent approach to estimate the impact of degradation on petroleum and to help reliably predict biodegradation risk at the prospect level. Our geochemical approach can be used to locate sweet-spots (areas of less degraded oil), optimize the placement of new wells and completion intervals and help with production allocation from long production wells. Introduction Biodegradation has a large influence on oil physical properties, which typically reduces oil producibility by increasing oil viscosity. Viscosity and density are key properties for the evaluation, simulation, and development of petroleum reservoirs. In order to develop and manage heavy oil fields cost effectively, it is essential to understand the variation in petroleum fluid properties, especially viscosity throughout each reservoir within a field. A variety of studies demonstrated how oil properties in biodegraded oil accumulations can be predicted from core and cutting extracts prior to well testing using geochemical parameters sensitive to biodegradation1–5. McCaffrey et al. 2 identified geochemical parameters that are sensitive to the degree of oil biodegradation and to the quantity of the secondary charge and then developed transforms that related those geochemical parameters to oil quality. Those transforms were used to predict oil quality from geochemical analysis of sidewall cores. Smalley et al. 3 used a similar approach to predict oil viscosity in a biodegraded heavy oil accumulation. Guthrie et al. 4 developed a predictive model of oil quality based on a sample set of produced oils from Venezuela for predicting viscosity, API gravity, and sulphur content in oil-stained sidewall cores where these properties cannot be measured directly. Koopmans et al. 5 analyzed oils from a single oilfield in the Liaohe basin, NE China. They found the large variations in viscosity across the field can be explained by mixing, to various extents, of heavy biodegraded oils with less degraded oils. They established a simple binary mixing model, which may assist in predicting the viscosity of reservoired oils before production.

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 score0.261
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
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.212
Teacher spread0.197 · 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

Citations16
Published2006
Admission routes1
Has abstractyes

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