Evaluation of Gas Assisted Gravity Drainage GAGD in Naturally Fractured Reservoirs NFR
Bibliographic record
Abstract
Abstract The EOR activity has been very restricted in naturally fractured reservoirs (NFR) because the fluid behavior on these reservoirs are strongly dependent of specific properties of the fractures such as direction, length, thickness, morphology and angle, and good tools were not available to get this information accurately from the reservoirs in the past. Today it is possible to get a lot of information on the fractures by using direct data sources like core samples, drill cuttings and downhole cameras; or even by indirect data sources like well log, well drilling, production history and seismic. These advancements in the data acquisition have facilitated EOR applications and the EOR activity has increased in the past few years in NFR. GAGD is a promising technique that uses the gravitational flow to improve the sweep efficiency in the reservoir and increase oil recovery. Its benefits have been proven in some academic works and in field applications, such as the Cantarell Oil field that achieved outstanding results. In this work, we have developed an experimental model that simulates the flow behavior of NFR from a Brazilian offshore oilfield. The model uses rectangular Berea Sandstones blocks that simulate the matrix rock in the experiments and these blocks are separated by small gaps using metal spacers. These gaps act as the fractures in the experimental model. The experimental conditions are close to the reservoir and the used configuration simulates the interaction between matrix and fracture, as well as the flow in the fractures. In the experiments the blocks are packed in a high-pressure physical model in the desired configuration. The gas is injected from the top and the oil is produced from the bottom. This work investigated the influence of the gas injection rate on oil production. The experiments were history matched using the commercial numerical simulator GEM from CMG. The experimental results showed good oil recovery performance with recovery factor as high as 40 per cent of OOIP, and it was observed that this value increases when higher gas rates are used. The numerical simulator has some limitations but provided a good history match with the results of the experiments. This paper proves the efficiency of the CO2 injection in NFR and also presents a new procedure for experimental modeling of fractured systems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".