Reservoir Characterization Through Single-Well Numerical Simulation Study Using DST Matching for a Gas-Condensate Reservoir
Bibliographic record
Abstract
Abstract This paper applies a single well numerical model using a reservoir simulator and accommodating geological data such as depth structure, gross and net thickness, and distribution of petrophysical properties to interpret a DST data. History matching pressures and rates of the DST data is conducted after incorporating the geological, reservoir engineering, and production data. In this single well simulation study, three DST data from well North Belut 3 are as the matching target used in the history matching using commercial numerical simulator with black oil formulation and three dimensional models. The PVT data is generated from an EOS model in which pseudoization is applied to reduce the components into 9. A one and a half foot model, or total of 1309 Z-grid dimension, is used to accommodate facies inconsistency and fluid gradient changes. Capillary pressures are obtained from mercury injection laboratory experiment of samples from four wells and are distributed according to permeability range values. On the other hand, the permeability curves are generated using the Corey function. The interpretation through the history matching process is described in steps to show advantages of using this method. Correct PVT fluid type, absolute permeability, and skin factor are the most affected on the pressure and rate matching process. The steps could explain and improve the understanding of reservoir characterization. The results are also compared with the analytical interpretations, which study is already done before by other person, to see the reliability. A good agreement on the permeability value is gained, with a possiblility of different net to gross ration interpretation. The skin factors of the numerical method are reasonably positive values, and tend to much less than the analytical results. This SPE-93218 suggests that the near well bore damage may not as bad as the analytical interpretation implied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".