Influence of well management in the development of multiple reservoir sharing production facilities
Bibliographic record
Abstract
Well prioritization rules on integrated production models are required for the interaction between reservoirs and restricted production systems, thus predicting the behavior of multiple reservoir sharing facilities. This study verified the impact of well management with an economic evaluation based on the distinct prioritizations by reservoir with different fluids. We described the impact of the well management method in a field development project using a consolidated methodology for production strategy optimization. We used a benchmark case based on two offshore fields, a light oil carbonate and a black-oil sandstone, with gas production constraint in the platform. The independent reservoir models were tested on three different approaches for platform production sharing: (Approach 1) fixed apportionment of platform production and injection, (Approach 2) dynamic flow-based apportionment, and (Approach 3) dynamic flow-based apportionment, including economic differences using weights for each reservoir. Approach 1 provided the intermediate NPV compared with the other approaches. On the other hand, it provided the lowest oil recovery. We observed that the exclusion of several wells in the light oil field led to a good valuation of the project, despite these wells producing a fluid with higher value. Approach 2 provided the lower NPV performance and intermediate oil recovery. We found that the well prioritization based on flow failed to capture the effects related to the different valuation of the fluids produced by the two reservoirs. Approach 3, which handled the type of fluids similarly to Approach 1, provided a greater NPV and oil recovery than the other approaches. The weight for each reservoir applied to well prioritization better captured the gains related to different valuation of the fluids produced by the two reservoirs. Dynamic prioritization with weights performed better results than fixed apportionment to shared platform capacities. We obtained different improvements in the project development optimization due to the anticipation of financial returns and CAPEX changes, due mainly from adequate well apportionment by different management algorithm. Well management algorithms implemented in traditional simulators are not developed to prioritize different reservoir wells separately, especially if there are different economic conditions exemplified here by a different valuation of produced fluids. This valuation should be taken into account in the short term optimization for wells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".