Updating Reservoir Simulation Models with Well Test Information for Reduction of Uncertainties in Early Field Development
Bibliographic record
Abstract
Abstract The high level of uncertainties during early phases of oilfield projects makes economic decisions challenging. One effective way to reduce uncertainties is gathering reservoir information from dynamic sources, such as well tests and production logging. This data must be incorporated into reservoir characterization integrated studies to generate probabilistic simulation models (scenarios). The objective of this work is to develop a procedure to update reservoir simulation models during reservoir characterization including well test and production logs, aiming better production forecasts. The proposed methodology consists in generating phi vs. log(k) equations derived from well test and production logging interpretation to update the permeability distribution in probabilistic scenarios without losing geological consistency. We also establish criteria for selecting wells to be tested based on openhole log data. We evaluate the result of the well this data incorporation in a synthetic field based on data from a real reservoir located in Campos Offshore Basin, Brazil. Then, we compared the results with the same reservoir model using classical phi-log(k) equations from laboratory experiments. Results showed that well test and production logging incorporation can improve the performance of the field measured by the risk curve of the net present value compared to the case without tests. We also showed how the history matching of the pressure derivatives can reduce variability of the prediction of the reservoir future behavior. The main contribution of this work is to evaluate how new information derived from well test and production logging improves the consistency of geomodels in early development of petroleum fields. Besides this improvement, the results also indicate that this methodology is an efficient way of reducing variability in the production forecast when integrated to other techniques such as history matching. The use of a benchmark case allowed us to show the influence of the quality of the simulation model in the process.
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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 | 0.001 |
| 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".