A Method for Probabilistic Forecasting of Oil Rates in Naturally Fractured Reservoirs
Bibliographic record
Abstract
Abstract A method is presented for probabilistic forecasting of oil rates in naturally fractured reservoirs (NFRs) based on a new modified Darcy equation. The equation includes variables that have significant impact on productivity of NFRs such as matrix and fracture permeabilities, partitioning coefficient, i.e, the ratio of fracture and total porosity, and thickness of the perforated interval. To perform the calculations, probability distributions are generated for each one of the above input variables. The locations of the water-oil and gas-oil contacts are determined, as well as the maximum efficient oil rates to avoid premature coning of water and gas. The integration of these data with the modified Darcy equation leads to prediction of maximum, most likely and minimum oil rates that could be anticipated for production wells in different parts of the reservoir. The difficulties and uncertainties that exist for predicting oil rates in NFRs are widely recognized. The proposed methodology looks to diminish this uncertainty with a view to generate reliable production forecasts, optimize oil rates, improve oil recovery and maximize economic returns. The modified Darcy equation for NFRs discussed in this paper is not presently available in the literature. The equation is easy and rapid to use statistically, and is being applied in practice with a good level of success. Thus, it provides a valuable supplement to numerical simulation and a significant reservoir engineering tool in those instances where operational decisions must be made quickly.
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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 | 0.000 | 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 | 0.000 |
| 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".