Performance and Economic Evaluation of Cyclic-Pressure Pulsing in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Summary Gas cyclic-pressure pulsing is an effective improved-oil-recovery (IOR) method in naturally fractured reservoirs. A limited number of studies concerning this method in the literature focus on specific reservoirs, yet the optimum operating conditions have not been broadly investigated. In this study, we present a detailed parametric study of the process from both operational and reservoir perspectives. Incremental oil production, discounted incremental oil production, and net present value (NPV) are considered as the important markers for the performance criteria. The necessary analyses are performed using a single-well, dual-porosity, compositional reservoir model. In the first part of the study, parametric studies are conducted to develop a better understanding of the operational parameters affecting the process performance in the shallow, naturally fractured, and depleted reservoir of Big Andy field in eastern Kentucky, USA. These include analyses of various design parameters (e.g., soaking period, cycle rate limit, number of cycles, cycle, and cumulative injected-gas volumes). In the second part of the study, reservoir characteristics are investigated. Comparative discussions are presented between cases with CO2 and N2 as the injected gas on reservoir fluids of different compositions (heavy, black, and volatile oils). Influences of area, thickness, fracture/matrix permeabilities, initial reservoir pressure, and temperature on the process are studied. It is observed that N2, as a lower-cost gas, would be a better choice than CO2 in the Big Andy field. With the oil price used in this study, the cost of injected gas becomes relatively insignificant in economic considerations. Increased income from increased oil production overcomes the increased costs with higher volumes of gas. The way reservoir characteristics affect the process performance is similar in cases with CO2 and N2, but differs significantly with different reservoir fluids. Thicknesses ranging between 20 and 50 ft produced more favourable results than thicker reservoirs. A higher efficiency was observed with smaller drainage areas (5 to 8 acres) in the presence of heavy oil. For the cases with volatile and black oil, it is observed that the process efficiency is not altered significantly by the area. The phase behaviour of the reservoir fluid is important for the performance of the process. Initial pressure/temperature of the reservoir and, therefore, the initial fractions of gas/liquid phases affect the process efficiency in a more pronounced manner.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".