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Record W1964856177 · doi:10.2523/iptc-12833-ms

Feasibility Study of the Cyclic VAPEX Process for Low-Permeable Carbonate Systems

2008· article· en· W1964856177 on OpenAlexaboutno aff
M.. Feali, Riyaz Kharrat

Bibliographic record

VenueInternational Petroleum Technology Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringSolventEnhanced oil recoveryPetroleumViscous fingeringViscosityCarbonateOil reservesEnvironmental scienceExtraction (chemistry)Process (computing)Materials scienceProcess engineeringChemistryComputer scienceGeologyChromatographyEngineeringComposite materialPorosityOrganic chemistryPorous medium

Abstract

fetched live from OpenAlex

Asbtract Vaporized hydrocarbon solvents injection in low permeable carbonate systems poses a serious challenge to petroleum engineers as well as a potentially effective and efficient oil recovery method. Thus, there is an incentive for the development of better vaporized solvent injection techniques since heavy oil reservoirs drastically draw attention of huge oil and gas companies. Cyclic VAPEX could be a key tool to achieving economic production from reservoirs containing very viscous oil. The rate of transfer of the solvent molecules in the crude directly reflects on extraction rate, therefore, cyclic process gives more time for the solvent to diffuse into heavy oil that it can be very interesting in very low permeability formations. Furthermore, breakthrough can be controlled by solvent injection rate during intervals as well as releasing surface facilities during the period of soaking being important from economical point of view. In this paper, a comprehensive study is conducted in order to understand the effects of cyclic injection operation properties; moreover, to show the capability of combination of immiscible displacement process with diffusion phenomena, different solvent compositions for each cycle were conducted. Also, the possibility of enhancing the extraction rate by combining of continuous and cyclic injection systems is investigated. This comparative sensitivity analysis is performed using 2D model including actual characteristics of a heavy oil reservoir of south west of Iran with oil gravity about 7–10 °API, and viscosity about 2000 cp. Introduction Recovery of the huge reserves of highly viscous heavy oil and poses a serious challenge to the engineers. Heavy oil reserves can only be recovered with low recovery efficiency by conventional methods. Primary recovery in the best of these heavy oil reservoirs can yield about 6% of the original-oil-in-place. Thermal stimulation of heavy oil-producing wells by cyclic steam injection has received attention since early 1960's.Currently, steam stimulation is being applied on a commercial scale, practically in Venezuela, California and Canada. With arrival of horizontal well technology, the production from heavy oil reservoirs has been considerably improved. Horizontal wells represent an indispensable technology for the production of heavy oil and extra heavy oil formations. Thermal process like SAGD and non-thermal process like VAPEX have been specially designed using horizontal wells for recovery of oil that is immobile at original reservoir conditions. In non-thermal oil recovery like cyclic solvent injection and VAPEX, horizontal wells have notable advantages over vertical wells such as better molecular diffusion and lateral transportation of fluids. Success in the combination of cyclic solvent injection with horizontal wells depends upon an appropriate technical and economic design. To the best of our knowledge, no optimization methodology has been developed to support design decisions about non-thermal simulation of horizontal wells. Among the complication associated with this task are the lack of field experience under a wide range of conditions, and the lack of numerical solution to predict the oil recovery from nonthermally stimulated horizontal well. Therefore, it is an interesting challenge and an imminent necessity to investigate numerical simulation of cyclic solvent injection and study the process of each cycle and design the conditions of each cycle which improve production rate such as soaking time, amount of solvent and composition of each solvent.

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 score0.036
Threshold uncertainty score0.452

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 science0.0010.000
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 head0.041
GPT teacher head0.299
Teacher spread0.258 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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