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Record W2093514844 · doi:10.2118/04-05-tn3

Low Field NMR Water Cut Metering

2004· article· en· W2093514844 on OpenAlexafffund
I.W. Wright, D. Lastockin, K. Allsopp, M.E. Evers-Dakers, Apostolos Kantzas

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsUniversity of CalgaryCanadian Natural Resources
FundersUniversity of Calgary
KeywordsHydraulic fracturingPetroleum engineeringFracture (geology)MechanicsFinite element methodFluid dynamicsMetering modeGeologyGeotechnical engineeringFlow (mathematics)Extraction (chemistry)Stress (linguistics)EngineeringMechanical engineeringStructural engineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Enhancing oil extraction from oil sands with a hydraulic fracturing techniquehas been widely used in practice. Due to the complexity of the actual process, modelling of hydraulic fracturing is far behind its application. Reproducingthe effects of high pore pressure and high temperature, combined with complexstress changes in the oil sand reservoir, requires a comprehensive numericalmodel which is capable of simulating the fracturing phenomenon. To capture allof these aspects in the problem, three partial differential equations, i.e., equilibrium, flow, and heat transfer, should be solved simultaneously in afully implicit (coupled) manner. A fully coupled thermo-hydro-mechanical fracture finite element model isdeveloped to incorporate all of the above features. The model is capable ofanalyzing hydraulic fracture problems in axisymmetric or plane strainconditions with any desired boundary conditions, e.g., constant rate of fluidinjection, pressure, temperature, and fluid flow/thermal flux. Fractures can beinitiated either by excessive tensile stress or shear stress. The fractureprocess is simulated using a node-splitting technique. Once a fracture isformed, special fracture elements are introduced to provide in-planetransmissivity of fluid. Effectiveness of the model is evaluated by solvingseveral examples and comparing the numerical results with analytical solutions.The model is also used to simulate large-scale laboratory hydraulic fracturingexperiments. Introduction Hydraulic fracturing technique has been a fast growing technology since itsfirst application in 1947. By 1988, more than one million hydraulic fracturingtreatments had been performed(1), and today this technique is one ofthe most important methods in enhancing oil extraction from wells. Hydraulicfracturing in oil and reservoirs plays an even more important role. Due to lowtemperature and low permeability of oil sand deposits and high viscosity ofbitumen, oil is virtually immobile(2). Hence, any attempt for insitu oil extraction should employ one of the following techniques: cyclic steamstimulation, in situ combustion, or hydraulic fracturing. Despite the fact that hydraulic fracturing technology has advancedsignificantly over the past fifty years, our ability to model the process hasnot changed as rapidly. As a matter of fact, this technique has been sosuccessful that in the past, designing the treatment with a high degree ofprecision was not of any interest. ut as the industry moved towardsapplications of very high volume/rate, and highly engineered and sophisticatedhydraulic fracturing treatments, the demand for more rigorous designs in orderto optimize the procedure have become more important. On the other hand, without a thorough understanding of the physical process and the factors thatare involved, our ability for an optimal design is limited. Modelling fluidflow combined with heat transfer in the reservoir has been used by the industryfor a long time, and the fracturing process was often designed based ontwodimensional closed-form solutions, such as Geertsma-deKlerk(3), or GdK in brief, and Perkins-Kern(4) and Nordgren(5), or PKN.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.005
GPT teacher head0.251
Teacher spread0.247 · 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 designBench or experimental
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
Published2004
Admission routes2
Has abstractyes

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