Integrated Operations (IO) in Mining and Oil and Gas, What can we learn from each other?
Bibliographic record
Abstract
Abstract The objective of the paper is to compare and contrast the progress made in the implementation of IO in mining and oil and gas. IO or Digital Oilfield (DOF) has been a major performance improvement initiative in the oil and gas sector for over ten years with many examples now in place. IO for mining has seen a slower uptake with relatively few examples over the last five years. This paper will cover IO implementation examples from Mining in Australia and Chile and in Oil and Gas from Australia, Canada and Europe. This paper will explore the similarities and difference in approach for the implementation of IO in mining and Oil and Gas. Specifically it will discuss: Common drivers for IO in mining and oil and gas. Similarities between the application of IO Differences in the application of IO Single site versus a value chain approach. Remote control versus an operational support model. Characteristics of a successful implementation in both Mining and Oil and Gas. Key common lessons learned. The results will be drawn from IO implementations in Iron Ore and Copper mining and from land based LNG and oil sands operations. The following items will be covered: The use of remote collaboration, operations and engineering support in both mining and oil and gas. The use of remote control in land based operations in both mining and oil and gas. A comparison of the LNG value chain with the Iron Ore value chain. A comparison of the water cycle management in copper mining with the steam management in SAGD operations. Key conclusion on the similarities and difference of IO in mining and oil and gas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".