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Record W4296406791 · doi:10.2118/212268-pa

New Insights into the Understanding of In-Situ Combustion: Important Considerations When Modeling the Process

2022· article· en· W4296406791 on OpenAlexaffabout
D. Gutiérrez, D. G. Mallory

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2022
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProcess (computing)CombustionEnhanced oil recoveryPetroleum engineeringProcess modelingProcess engineeringRange (aeronautics)Computer scienceBiochemical engineeringEngineeringWork in processChemistryAerospace engineeringOperations management

Abstract

fetched live from OpenAlex

Summary Air-injection-based enhanced oil recovery (EOR) processes have historically been of great interest due to their high recovery potential and applicability to a wide range of reservoirs where other processes are not effective or economical. However, most operators require a certain level of confidence in the potential recovery from these (or any) process before committing resources; this is commonly achieved with the support of laboratory and reservoir simulation studies. Laboratory testing, including combustion tube, ramped temperature oxidation (RTO), and accelerating rate calorimeter (ARC) tests, can supply data for simple analytical models. It can also provide important insights into potential oxidation behaviors and oil recovery mechanisms. Similarly, reservoir simulation of some of those experiments can assist in the understanding of the process and may allow for the development of kinetic models that can be used for further reservoir modeling. However, due to sample size limitation and the unscaled nature of the experiments, these tests are not ideally suited to provide detailed or unique kinetic data for direct use in numerical simulators. In fact, the oxidation reactions are sufficiently complex that, regardless of how robust a thermal reservoir simulator may be, its predictive capability strongly depends on the engineer’s understanding of the process and ability to model the most relevant oxidation behaviors of the particular hydrocarbon reservoir under study. Over the past 50 years, the In-Situ Combustion Research Group (ISCRG) at the University of Calgary has dedicated its efforts toward the advancement of this technology. Under the leadership of Professor Gordon Moore, the ISCRG has performed a large number of combustion tests, designed and carried out many novel oxidation experiments, and also made important contributions to the numerical modeling of air-injection-based processes. Nevertheless, in spite of its long research history, the group acknowledges that there is still much that needs to be learned about the process. For example, two oils with the same physical properties such as viscosity and density can have significantly different oxidation behaviors, which are difficult to predict; this is one of the reasons the group continues to perform laboratory experiments and conduct research in this area. This paper describes some of the most important conceptual contributions made by the ISCRG based on their experimental results and how they have enhanced our understanding of the process. These continue to be an important source of knowledge toward the development of predictive reservoir simulation models, as it is very difficult, if not impossible, to properly model a physical problem one does not understand well. For instance, the fundamental equations used for mathematical modeling depend on selecting of the relevant physical mechanisms and assumptions made, and these are derived from experimental work. Similarly, when using a commercial numerical simulator, the selection of fluid pseudocomponents as well as their physical properties and chemical reactions, as well as their kinetic parameters, also depend on an understanding of the process. This paper provides a summary of the relevant physical aspects to consider when modeling the in-situ combustion (ISC) process as well as new insights on its dynamics based on the laboratory experiments performed by the ISCRG.

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)0.001
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 score0.052
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.059
GPT teacher head0.295
Teacher spread0.236 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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