Reasonable Productivity Calculation and Sensitivity Analysis of Horizontal Wells in Overseas Carbonate Reservoir
Bibliographic record
Abstract
Unlike domestic carbonate reservoirs, overseas carbonate reservoirs typically exhibit significant lithological variations and stronger heterogeneity. There is a lack of effective methods for energy supply and techniques to enhance oil recovery. Additionally, acquiring data during offshore platform operations presents significant challenges. Evaluating field productivity solely based on existing data remains problematic. To address this, we integrate drill stem test (DST) data and reservoir numerical simulation to calculate productivity and conduct sensitivity analysis. Initially, the DST data are collected for pressure transient analysis to estimate reservoir permeability and skin factors, which enables reasonable single‐well productivity predictions. Subsequently, detailed reservoir numerical simulations are utilized to investigate the effects of liquid production rate, production pressure drop, horizontal section length, and perforation position on field productivity, thus guiding optimal production design. Results indicate that the combination of DST data and numerical simulations is essential for accurately assessing productivity in carbonate reservoirs and supporting efficient development. With an increasing liquid production rate, cumulative oil production gradually rises, plateauing when it exceeds 7%. As production pressure drop and horizontal section length increase, the recovery factor improves up to an optimal value. Improper perforation positions, either too low or high, reduce cumulative oil production and 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".