Unified Theory of Ultimate Hydrocarbon Recovery for Primary and Cyclic Injection Processes in Ultratight Reservoirs
Bibliographic record
Abstract
Abstract This paper presents a simple method to estimate ultimate recovery factors (URF) of ultratight reservoirs based on equilibrium by diffusion in which URF is only a function of changes in hydrocarbon density between initial and final states. URF is defined at infinite time and therefore does not depend on the transient behavior. Although URF may not be achievable during the life-cycle of the field development and production, it provides valuable insights on the role of phase behavior. We show that equilibrium phase behavior defines the absolute upper-bound for URF during primary production and explains the poor recovery from shale oil reservoirs compared to the high recovery factor in shale gas reservoirs in a unifying way. Further, we quantify how injected solvent compositions (CH4, CO2, N2, and C2H6) during huff’n’puff enhanced oil recovery (EOR) improve recovery based on density reduction and compositional dilution, and show that the largest percentage increase in recovery occurs for heavier oils. Our calculations provide a practical means to define the URF from primary production as a function of reservoir fluid composition, temperature, and pressure drawdown. In addition, our calculations articulate incremental URF (IURF) of solvent huff‘n’puff based on net solvent transfer into ultratight rock, which is a key design consideration. The results illustrate that solvent transfer dilutes the hydrocarbons in place, thus maximizing long-term hydrocarbon recovery. Net mass transfer can be improved by enhancing the diffusion of solvent into the matrix based on the huff‘n’puff design parameters including solvent composition, drawdown pressure, and the net amount of solvent injected based on optimal frequency and cycle duration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".