Estimating Oil Saturation Index OSI from NMR Logging and Comparison with Rock-Eval Pyrolysis Measurements in a Shale Oil Reservoir
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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