Improvement to Gravity Drainage Recovery by Repressurization as a Criterion to Screen and Rank Naturally Fractured Reservoirs for Gas Injection
Bibliographic record
Abstract
Summary Many of the naturally fractured carbonate reservoirs of the Middle East exhibit low natural-depletion recoveries. The reason is that most of their oil reserves are stored in the low-permeability host rocks and are left behind by the fast advancing gas/oil contact (GOC) and water/oil contact (WOC) in fractures. Producing the remaining oil in the large gas-invaded zone of these reservoirs has been a crucial reservoir management issue. We show in this study using experimental observations, analytical calculations, and numerical investigations that repressurizing naturally fractured reservoirs (NFRs) by crestal immiscible gas injection has the potential to produce a large portion of this remaining oil by improving gravity drainage (GD) through two main mechanisms. One is that at higher pressures, the gas-oil interfacial tension (IFT) and hence the capillary forces that control recovery by GD are lessened, allowing additional recovery. This mechanism is aided by the other one, which is swelling of the oil at higher pressures. In this way, repressurization is thought to be not only a means for pressure maintenance but also a methodology for enhanced-oil recovery (EOR). This is confirmed by both laboratory studies and field performance of large-scale gas injection projects. Despite the desire for implementation of projects of repressurization, gas availability and cost of these projects are important concerns, requiring a cost-benefit analysis. Screening and ranking methodologies have been previously presented for some EOR techniques but not for repressurization by gas in NFRs. Evaluating the performance of gas injection in NFRs is often done using methodologies such as numerical simulations, which are in-depth, costly, and tedious. The methodology developed here is simple, requiring spreadsheet calculations. To develop the methodology, we first obtain simple relations to calculate additional GD recovery by considering the interplay of capillary and gravity forces in a matrix block subjected to pressurization by equilibrium gas injection and then use experimental data from literature to show that these relations can predict primary and secondary GD recoveries to a good approximation. We also show by mechanistic studies using a history-matched numerical model that IFT reduction and oil swelling are the main mechanisms contributing to additional oil recovery. Then, we propose a methodology to screen and rank candidate NFRs for gas injection that uses commonly available reservoir data and is based upon two criteria, these being additional oil recovered from a matrix block by pressurization and required volume of gas to produce an additional barrel of oil. We then implement this methodology to more than 20 Iranian NFRs and identify six reservoirs with potential for additional recovery of more than 20%. By quantifying and including the uncertainties associated with the reservoir data, we illustrate that for the reservoirs under study, capillary pressure parameters along with matrix-block height are the main parameters affecting GD recovery and should be characterized more accurately.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".