Impact of Entrained Hydrocarbon and Organic Matter Components on Reservoir Quality of Organic-Rich Shales: Implications for Sweet Spot Identification in the Duvernay Formation Canada
Bibliographic record
Abstract
Abstract Hydrocarbon storage capacity of organic-rich shales depends upon porosity and surface area, whereas pore (throat) size distribution and pore (throat) network connectivity control permeability. The pores within organic matter (OM) of organic-rich shales develop during thermal maturation as different hydrocarbon phases are generated and expelled from the OM. Organic-rich shales can potentially retain a large proportion of the hydrocarbons generated during the diagenesis process. Commercial hydrocarbon production from liquid-rich shale reservoirs can be achieved using completion technologies such as multi-stage-fractured horizontal wells (MFHWs). However, the ability of industry to identify "sweet spots" along MFHWs is still hampered by insufficient understanding of the effect of type/content of entrained hydrocarbon/OM components on reservoir quality. The primary objective of the current study is therefore to investigate the impact of entrained hydrocarbon/OM on storage and transport properties of the organic-rich shales. To accomplish this goal, a comprehensive suite of petrophysical analyses are performed on a diverse sample suite from the Duvernay Formation (a prolific Canadian shale oil reservoir) differing in organic matter content (2.8-5 wt.%; n = 5), before and after sequential pyrolysis by a revised Rock-Eval analysis (extended slow heating (ESH) Rock-Eval analysis; Sanei et al., 2015). Using the ESH cycle, different hydrocarbon/OM components can be distinguished more easily and reliability during the pyrolysis process: 1) free light oil (S1ESH; up to 150 °C), 2) fluid-like hydrocarbon residue (S2a; 150-380 °C) and 3) solid bitumen/residual carbon (S2b; 380-650 °C). The characterization techniques at each stage are helium pycnometry (grain density, helium porosity); low-pressure gas (N2, CO2) adsorption (pore volume, surface area, pore size distribution within micropores, mesopores and smaller macropores); crushed-rock gas (He, 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. Compared to the "as-received" state, porosity, permeability, modal pore size distribution and surface area increase with sequential pyrolysis stages, associated with expulsion and devolatilization of free light oil and fluid-like hydrocarbon residue (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 the S2b stage are likely attributed to 1) occlusion of pore volume with solid bitumen/residual carbon degradation (i.e. coking) and/or 2) sample swelling due to water loss from lattice structure of clay minerals (i.e. illite) and 3) sample compaction as a result of OM removal from the rock matrix. The present study is a continuation of previous works (Clarke et al., 2016, 2017), aiming to elucidate the impact of different type and content of entrained hydrocarbons/OM on reservoir quality of organic-rich shales. Quantification of the evolution of reservoir quality with thermal maturity has important implications for 1) identifying petrophysical "sweet spots" within unconventional reservoirs, for the purpose of optimizing stimulation design and 2) targeting specific zones within the reservoir of interest with organic matter content/type amenable to maximizing gas storage/transport during cyclic solvent injection for enhanced oil recovery applications. The integrated workflow proposed herein is of significant interest to Duvernay operators for developing optimized stimulation treatments for improving primary and enhanced hydrocarbon recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".