Deciphering the Fundamental Controls of Flow in Carbonates Using Numerical Well-Testing, Production Optimisation, and 3D High-resolution Outcrop Analogues for Fractured Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Carbonate reservoirs contain a significant portion of the world’s proven hydrocarbon reserves but are challenging to produce due to their complex lithologies, structural heterogeneities, and neutral to oil-wet nature. Increasing recovery requires a better understanding of how different recovery processes respond to the heterogeneities inherent to these reservoirs. This will contribute to the design of appropriate engineering solutions which extend the life of mature fields and develop green fields more effectively. We use a high-resolution 3D outcrop model of a Jurassic carbonate ramp in order to perform a series of detailed and systematic flow simulations. The aim is to test the impact of small- and large-scale geological features on reservoir performance and oil recovery. The outcrop analogue model is of excellent quality comprising a wide range of diagenetic and structural features, including discontinuity surfaces, mud mounds, mollusc banks and fractures. Flow simulations are performed for numerical well-testing and secondary oil recovery. Numerical well-testing allows us to generate synthetic but systematic pressure responses for different geological features observed in the outcrops. This allows us to assess and rank the relative impact of specific geological features on reservoir performance. The outcome documents that, due to the high level of matrix heterogeneity, most diagenetic and structural features cannot be linked to a unique pressure signature. Instead, reservoir performance is controlled by sub-seismic faults and mollusc banks acting as thief zones. Numerical simulations of secondary recovery processes reveal strong channelling of fluid flow into high-permeability layers. This is the primary control for oil recovery. However, appropriate reservoir engineering solutions such as optimising well placement and injection fluid can reduce channelling and increase oil 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".