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Record W2560427188 · doi:10.2118/184161-ms

Modeling Heavy-Oil Recovery Using Electromagnetic Radiation/Hydraulic Fracturing Considering Adiabatic Effect and Thermal Expansion

2016· article· en· W2560427188 on OpenAlex
A. Ya. Davletbaev, Л. А. Ковалева, А В Зайнулин, В. Н. Киреев, Tayfun Babadagli, Р. З. Миннигалимов

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Heavy Oil Conference and Exhibition · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
FundersMinistry of Education and Science of the Russian FederationRussian Foundation for Basic Research
KeywordsAdiabatic processPetroleum engineeringMechanicsDielectric heatingThermalMaterials scienceNuclear engineeringEnvironmental scienceThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Production of heavy oil from deep/tight formation using traditional technologies ("cold" production, injection of hot steam, etc.) is ineffective or inapplicable. An alternative is electromagnetic (EM) heating after fracturing. This paper presents the results of a numerical study of heavy oil production from a well with hydraulic fracture under radio-frequency (RM) electromagnetic radiation. Two parameters ignored in our previous modeling studies, namely adiabatic effect and the thermal expansion of oil, are considered in the new formulation while high gradients of pressure/temperature and high temperature occur around the well. The mathematical model is simulated distribution of pressure and temperature in the system of "well-fracture- formation". The distribution of thermal heat source is given by the Abernetty expression. The mathematical model takes into account the adiabatic effect and thermal expansion of heavy oil. The latter makes a significant contribution to heavy oil production. Multi-stage heavy production technology with heating is assumed and several stages are recognized: Stage 1: "Cold" heavy oil production, Stage 2: RF-EM heating, Stage 3: RF is turned off and "hot" oil production continues until the flow rate reaches its initial (before heating) value. These stages are repeated starting from the second stage. Finally, RF-EM heating technology is compared to "cold" production in terms of additional oil production and economics. When producing with RF-EM heating with power 60 kW (50 days in the second stages), the oil rate increased several times. Repeated RF-EM heating (25 days in the fourth stage) doubled the production rate. Near-well region temperature increased by ~82°C in the second stage with RF-EM heating. Temperature increased by ~87°C in the fourth stage with repeated RF-EM heating and production cycles. Economic analysis and evaluation of energy balance showed that the multi-stage production technology is more efficient; i.e., the lower the payback period, the greater the energy balance. With the increase in pressure difference, the payback period and energy balance increased linearly.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.955

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 communication0.0000.001
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 head0.012
GPT teacher head0.219
Teacher spread0.207 · 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