Bibliographic record
Abstract
Abstract Apache Energy has implemented a system which provides data from real-time well head process data to continuous month to date production information available to head office and joint venture partners over the internet. Apache is one of the largest gas produces in Australia with assets in Australia and overseas. There are several different Joint Ventures (JVs) involved in the various fields and processing plant. The operations are spread between Offshore, Varanus Island, Perth and Houston. Joint venture partners are located in Adelaide, US, Perth and Kuwait and gas customers are located throughout Western Australia. Such diversity requires effective information distribution. Over the last 5 years, Apache has implemented a process information management system, which enables production data to be transmitted securely all over the world, minutes after the end of each production day. Production data business flow commences with Apache's customers placing orders or "nominations" via the web based gas nominations system. The nominations are validated against the customer contracts, collated and the total gas production for the day is electronically issued to the operator. At the end of the gas day, production, well, and facility performance data is collected automatically and presented to the operator for validation. Once validated by operations, the data is loaded into the Production Reporting System (PRS) database and a series of applications are executed such as; Well allocationSales gas reconciliationJoint Venture partner splits.Environmental calculations The data is then released to the Apache users and the Joint Venture partners shortly after the end of the 08:00 production day. The data is also released on to the Internet under a security system which allows Joint Venture partners to view production, inventory/lifting and monetary splits between the JVs for the month. Environmental reports are issued to the government via internet pages. In addition to production data, the system provides engineers and management with remote adhoc real-time process data. Use of this system has enabled the engineering groups to identify well problems resulting in production benefits. Apache has been able to significantly reduce the amount of time required to provide JV reports and JV payments and double handling of data has been virtually eliminated. The goal of entering data once, at the source, has largely been met. This paper will address the processes and business benefits Apache have observed from this knowledge management system.
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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".