Generating Cost Reduction in the Supply Chain by Coupling Surface Facilities with Reservoir Data in Integrated Asset Modelling
Bibliographic record
Abstract
Abstract Integrated Asset Modelling (IAM) processes can be used to quantify and monetise reservoir potential during the concept design process. Through quantification, risks can be mitigated and managed to deliver greater certainty through the concept selection process. IAM can be deployed throughout the hydrocarbons upstream sector to deliver robust field development and concept design solutions with more robust project economics. IAM offers significant value and potential to identify supply chain cost reductions across the full spectrum of upstream developments, from full field development planning to individual equipment item modifications. The process offers particular value in development planning; in identifying and assessing sensitivity options and in definition of the optimum concept. Full IAM incorporates expertise in reservoir, flow assurance, facilities and project economics expertise into a single integrated asset model. The process defined here is most effective as it is built upon integrating industry and in-house software, a combination of which facilitates Life of Field analysis to be carried out within a single package. The process offers unparalleled speed and flexibility in identification and analysis of concept design sensitivity cases. The IAM approach offers significant advantages and benefits to operators in addressing the three main reasons for destruction of project value; poor estimation of reserves, schedule overruns and cost overruns. This IAM process is configured for fast and robust evaluation to ensure the right decisions are made at the right time based on the right information. The process will therefore preserve project value for operators and reduce the risk of value erosion through over or under engineering, or project recycle.
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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 | 0.002 | 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".