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Record W2586149758 · doi:10.2118/185073-ms

Estimating Oil Saturation Index OSI from NMR Logging and Comparison with Rock-Eval Pyrolysis Measurements in a Shale Oil Reservoir

2017· article· en· W2586149758 on OpenAlex

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 Unconventional Resources Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOil shalePorosityPetrophysicsBinPetroleum engineeringPyrolysisSaturation (graph theory)Well loggingMineralogyGeologyEnvironmental scienceGeotechnical engineeringWaste managementEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract Identification of potential oil flow zones in shale reservoirs has been conducted in the past with the use of an oil saturation index (OSI) determined from Rock-Eval pyrolysis measurements on samples collected at pre-specified depths (partial sampling). This study introduces a new equation that allow continuous OSI determination with the use of the Nuclear Magnetic Resonance (NMR) log. Geochemical analysis using measurements from Rock-Eval pyrolysis and LECO Carbon Analyzer laboratory techniques were carried out in a shale oil reservoir for estimating parameters such as total organic carbon (TOC) and OSI. This allowed identification of hydrocarbons zones. Next, Cross-over and OSI cut-off techniques were applied to distinguish intervals with producible and non-producible hydrocarbons. Subsequently, NMR total response relaxation time, T2, was divided into eight T2 cut-offs to calculate bin porosities. A sensitivity analysis for T2 cut-offs was run in order to establish a good match between the bin porosity and OSI values that indicate producible hydrocarbons. A good agreement was reached among OSI greater than 100 mg HC/gTOC and the bin porosities estimated between T2 = 33ms and 80 ms. This match was corroborated by the visual "oil cross-over" from geochemical analysis. An OSI cut-off equal to 100 mg HC/g TOC has been recommended in the past by several authors to differentiate producible from non-producible oil intervals. That cutoff compares well with the NMR bin porosity developed in this paper. Thus, the porosity estimation between above T2 cut-offs is a good indicator of producible hydrocarbons in a shale oil reservoir. This observation has led to the development of a new equation in this paper to convert the NMR bin porosity to OSI (or vice versa) continuously throughout the NMR logged interval. Also, if TOC is already known from a given method (for example, Passey, Smocker, GR spectral, Uranium), the S1 parameter can be estimated from only well logs resulting in continuous S1 and OSI curves. This is a very significant advantage since Rock-Eval pyrolysis and LECO analyzer are run on samples which are taken at predefined depths (partial sampling); therefore, possible producible oil zones could be bypassed if only core results are taking into account.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.880

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 head0.045
GPT teacher head0.268
Teacher spread0.223 · 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