Geochemical and Petrophysical Characterization of Canadian Low-Permeability Oil and Liquid-Rich Gas Reservoirs using Drill Cuttings
Bibliographic record
Abstract
Significant technology development has aided gas and oil production from Canadian ultra-low permeability (unconventional) reservoirs during the past twenty years. Multi-fractured horizontal wells (MFHWs) in particular have enabled commercial production from these reservoirs. However, hydraulic fracturing in MFHWs is commonly performed without consideration for reservoir quality variability along the lateral section of MFHWs. Identification of “sweet spots” along the lateral can help to target hydraulic fracture stages. Drill cuttings are, however, often the only reservoir samples collected from MFHWs. This thesis addresses the limitations of the currently available drill cuttings characterization techniques, and proposes new methods to estimate geochemical and petrophysical properties from this sample type. A predictive algorithm is developed for quantification of mineralogical composition from elemental data obtained using portable energy dispersive X-ray fluorescence (pXRF) and inductively coupled plasma mass spectrometry (ICP-MS) techniques. The development of this technique allows operators to acquire high-resolution mineralogical compositions along the length of MFHWs by conducting inexpensive, time-efficient, non-destructive elemental analysis. Another important contribution of this thesis is the establishment of an integrated experimental and modeling approach to estimate surface diffusion coefficients and permeability of porous media, including synthetic porous materials and drill cuttings collected from unconventional reservoirs. Currently, the commonly-used empirical methods for estimation of surface diffusion cannot incorporate the complexity of rock fabrics in unconventional reservoirs. To address this limitation, in this thesis, surface diffusion coefficients of the gas/solid system are estimated by history-matching adsorption rate data collected on small amounts of porous materials using a newly-developed rate-of-adsorption (ROA) model. Simulation results demonstrate the importance of surface diffusion under the applied experimental conditions. Finally, the impact of organic matter on 1) matrix permeability and 2) gas transport properties in macro-/meso-/micropores of Duvernay shale samples is investigated. The proposed ROA model is applied to 1) extract gas (N2/CO2) apparent permeability, and 2) investigate gas transport mechanisms. The evolution of pore attributes, permeability and flow regimes of shale samples are determined by subjecting shale samples to an Extended Slow Heating (ESH) Rock-Eval thermal treatment and measuring the properties after each treatment stage.
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How this classification was reachedexpand
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)
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".