Effects of Entrained Hydrocarbon and Organic-Matter Components on Reservoir Quality of Organic-Rich Shales: Implications for “Sweet Spot” Identification and Enhanced-Oil-Recovery Applications in the Duvernay Formation (Canada)
Bibliographic record
Abstract
Summary The hydrocarbon (HC)-storage capacity of organic-rich shales depends on porosity and surface area, whereas pore-throat-size distribution and pore-throat-network connectivity control permeability. The pores within the organic matter (OM) of organic-rich shales develop during thermal maturation as different HC phases are generated and expelled from the OM. Organic-rich shales can potentially retain a large proportion of the HCs generated during the diagenesis process. Commercial HC production from liquid-rich shale reservoirs can be achieved using completion technologies such as multistage-fractured horizontal wells. However, the ability of industry to identify “sweet spots” along multistage-fractured horizontal wells for both primary and enhanced oil recovery (EOR) is still hampered by insufficient understanding of the effects of type/content of entrained HC/OM components on reservoir quality. The primary objectives of the current study are therefore to establish an integrated experimental workflow to investigate the effect of entrained HC/OM on storage and transport properties of the organic-rich shales, and to provide examples of that experimental workflow through analyzing a selected sample suite from a prolific shale-oil reservoir (the Duvernay Formation) in western Canada. To accomplish this goal, a comprehensive suite of petrophysical analyses is performed on a diverse sample suite from the Duvernay Formation that differs in OM content (2.8 to 5 wt%; n = 5), before and after sequential pyrolysis by a revised Rock-Eval analysis [extended-slow-heating (ESH) Rock-Eval analysis]. Using the ESH cycle, different HC/OM components can be distinguished more easily and reliably during the pyrolysis process: free light oil (S1ESH; up to 150°C), fluid-like HC residue (FHR) (S2a; 150 to 380°C), and solid bitumen/residual carbon (S2b; 380 to 650°C). The characterization techniques used at each stage are helium pycnometry (grain density, helium porosity); low-pressure gas [nitrogen (N2), carbon dioxide (CO2)] adsorption (LPA) [pore volume (PV), surface area, pore-size distribution (PSD) within micropores, mesopores, and smaller macropores]; crushed-rock gas [helium, CO2, N2] permeability; and rate-of-adsorption (ROA) analysis (CO2, N2). Scanning-electron-microscopy (SEM) analysis is further conducted to verify/support the petrophysical observations. Powder X-ray-diffraction (XRD) analyses were performed on all samples in the “as-received” state and after Stage S2b (thermal pyrolysis up to 650°C) to quantify variations in mineralogical compositions and their possible controls on the evolution of petrophysical properties (i.e., porosity/permeability). Organic petrography was conducted on selected samples to characterize the nature of OM. Compared with the “as-received” state, porosity, permeability, modal-pore-size distribution, and surface-area increase with sequential pyrolysis stages, associated with the expulsion and devolatilization of free light oil and FHR (S2a; up to 380°C). However, the change in petrophysical properties associated with the degradation of solid bitumen/residual carbon (S2b; up to 650°C) is variable and unpredictable. The observed reduction in porosity/permeability values after Stage S2b is likely attributed to the occlusion of PV with solid bitumen/residual carbon degradation (i.e., coking); sample swelling caused by water loss from the lattice structure of clay minerals (i.e., illite); and sample compaction as a result of OM removal from the rock matrix. Among various stages of the ESH Rock-Eval pyrolysis, the petrophysical properties that are measured after Stages S1ESH and S2a, as they are related to the expulsion of the lighter and heavier free-HC compounds from the rock matrix, are expected to be the most important for primary and EOR applications. Quantification of the evolution of reservoir quality with HC generation/expulsion has important implications for identifying petrophysical “sweet spots” within unconventional reservoirs, optimizing stimulation design, and targeting specific zones within the reservoir of interest with the OM content/type amenable to maximizing gas storage/transport during cyclic solvent injection for EOR applications. The integrated experimental workflow proposed herein could be of significant interest to the operators of organic-rich shale/mudstone plays (e.g., the Duvernay) as a screening tool for developing optimized stimulation treatments for improving primary and enhanced HC recovery.
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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".