Evaluation of Produced Hydrocarbons Composition During Cyclic CO2 Injection (Huff-N-Puff) in Artificially-Fractured Shale Core Sample
Bibliographic record
Abstract
Abstract Natural and hydraulic fractures are important contributors to production performance of low-permeability (‘tight’) hydrocarbon reservoirs during primary and enhanced oil recovery. Laboratory studies that have investigated core-scale huff-n-puff (HNP) processes in ‘fractured’ cores are rare, and focused on ‘rock’ analysis primarily, as opposed to ‘fluid’ analysis. The objective of this proof-of-concept experimental study is to evaluate the application of a new core-scale HNP technique, ‘flow-through-frac’, for tracking compositional evolution of produced liquid hydrocarbons during cyclic gas (CO2 herein) injection in ‘fractured’ low-permeability oil reservoirs. The flow-through-frac technique reproduces the near-fracture conditions during a typical HNP process, with significantly faster testing times (25-50%) compared to conventional techniques (e.g., flow-around). The experimental procedure includes: 1) artificially fracturing core plug sample under differential stress to simulate an induced fracture, 2) saturating the fractured core with de-waxed in-situ (formation) oil, and 3) implementing multiple cycles of gas (e.g., CO2, produced gas) injection, soaking and production. To determine whether this technique can detect compositional variations despite its short duration, the compositions of the original in-situ (dead) oil and produced liquid hydrocarbon sample were compared after a typical core-scale HNP process (4 cycles) using CO2. A low-porosity (3.3%), low-permeability (1.25·10−4 md) Duvernay shale (western Canada) core plug sample was analyzed in this study. Compared to the in-situ (dead) oil, lighter components (C7-C11) were significantly (up to an order of magnitude) leaner in the oil sample produced after 4 cycles of CO2 HNP (fractured core plug). The lighter the hydrocarbon components, the leaner the concentrations in the produced oil. The intermediate components (C12-C28) were enriched in the produced oil, with larger discrepancies for C14-C22 components. The latter observation is attributed to the replacement of adsorbed C17-C19 components by injected CO2, in agreement with recent molecular simulation and experimental studies. The concentrations of heavier components (C29-C33) were similar between the in-situ and produced oil samples. Through combining core-scale CO2 HNP and fluid sampling/testing, this work demonstrates that the flow-through-fracture method can detect compositional variations during a typical core-scale HNP experiment. This technique can enable operators to track the composition of produced hydrocarbons at near-fracture conditions at a significantly shorter time frame (25–50%) than the existing methods. This integrated rock and fluid experimental program could potentially become valuable to not only core-based evaluation of enhanced oil recovery (EOR) in unconventional oil reservoirs but also potentially coupled CO2/produced gas EOR and sequestration processes in fractured shale reservoirs.
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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 | 0.000 | 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) | 0.001 | 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".