Analysis and Optimization of Zama Field Development Using Integrated Production System Modelling
Bibliographic record
Abstract
Abstract The Zama field, located offshore of Mexico, is one of the world's biggest shallow-water oil discoveries in the past 20 years. The field has recoverable volumes of over 700 MBBL of oil. The development plan will include 2 production platforms, 29 oil producers, and 17 water injectors with two 66 km long pipelines to carry the oil to an onshore facility. Given the significance of this field, the use of an IPSM (Integrated Production Systems Modelling) can provide an increased value from this development through better design and operational decisions. The reservoir has conventional 28 °API oil, located in a thick pay with a significant geothermal gradient - Which will have an impact on the performance of the waterflood as water properties will change with temperature. This requires thermal capability in the reservoir simulator. The wellbore and facilities models, including all relevant completions and equipment, were built in a steady-state integrated production system module, that is capable of handling any fluid model. For this project, thermal black oil in both reservoir and facilities was considered the right approach as it will have a better simulation performance than full EOS models. The integrated simulation used explicit coupling between the reservoir and production models, with a "smart" coupling frequency chosen by the integration tool. The workflow allows for multi-fidelity solution to IPSM - and this was utilized in areas such as well and pipeline models where there was a choice to use pressure drop correlations or pipe tables. Even though this is a greenfield development, the analysis had shown that water injection should begin from the start of field development. The base IPSM considered all these aspects, and was optimized for performance. Thereafter, the production and injection strategy (constraints, rates, scheduling, etc.) as well as the overall completion and facilities design (well tubings, pumps, pipeline, risers, etc.) were optimized in an integrated fashion - providing a range of outcomes from the chosen schemes. The workflow yielded a stable IPSM system capable of predicting long-term performance of the Zama field development plan. The workflow was able to integrate subsurface and surface disciplines on a collaborative platform, which drastically reduced the logistical and workflow inefficiencies that exist in traditional IPSM workflows. The advanced fluid handling capabilities, with thermal black oil models in both reservoir and production system proved valuable to enhance the predictability from the model. The workflow captured the complex interactions between facilities and reservoir and the entire system was optimized using a novel end-to-end uncertainty management framework.
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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".