Planning and optimisation of large, complex, low pressure gas and water gathering systems*
Bibliographic record
Abstract
Modelling low pressure gas gathering systems for unconventional gas (UCG) and coal seam gas (CSG) or coal bed methane (CBM) developments is challenged by: the sensitivity of well performance to system back-pressure;statistical uncertainties over well performance and areal variability in pay quality;the rapid build-up of friction with gas velocity;the rate of conversion from planning assumptions to real data on wells and lines;commissioning and initial well test reliability issues;modelling transient well performance using steady state solutions;unpredictable peak rates and rapid initial declines from those peak rates;well interference effects;large development areas with multiple gathering stations;line looping and bi-directional lines required to accommodate compressor outages; and,new gas treatment and transmission facilities being added onto existing system during development drilling operations. Moreover, many of these systems have multiple gas delivery points at different delivery pressures. Similarly, low pressure water gathering, storage, processing and delivery systems involve features that are not common in conventional water injection or salt water disposal networks, such as: large numbers of wells tied-in using lines with very low pressure ratings;water storage ponds or tanks that may involve evaporation losses;gravity feed or back-flow during intermittent pumping;infield modifications to accommodate overload situations that had not gone through a formal design change process; and,intermittent transfer operations to take advantage of reduced power costs during the night, or to avoid peak demand penalties. This paper will look at two case histories from North America that had to address many of these problems. The objective is to capture lessons learned to assist in the challenges that we are currently facing in ramping-up CSG production in eastern Australia.
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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".