A rapid and flexible method for simulation of CSG water production: application in the Surat and Bowen basins
Bibliographic record
Abstract
There is significant interest in estimating volumes of water extracted during production as the CSG industry develops in the Surat and Bowen basins in Queensland, Australia. Klohn Crippen Berger Ltd (KCB) was commissioned by the Queensland Department of Natural Resources and Mines (DNRM) to develop a tool to estimate where, when, and how much CSG water will be produced in these areas under various industry expansion scenarios. The tool, which is now being maintained and further developed to interface with GIS software by the Centre for Water in the Minerals Industry (CWiMI), was built to balance numerical complexity against relative flexibility and simulation speed. This was achieved by an approach that differs from conventional reservoir engineering models, including: the use of non-equilibrium groundwater flow equations (the Theis equation) in conjunction with semi-empirical type-curve based methods;calculation of well interference effects and corresponding spatial scaling effects in a relatively large-scale spatially discretised model; and,modification of flows predicted using the Theis equation to reflect the dual-phase nature of CSG extraction, and the unique hydrogeological setting of the eastern margin of the Surat Basin. The tool was verified against equivalent Theis equation calculations and type curves provided by CSG proponents. The tool was demonstrated to adequately represent the unique physical mechanisms of CSG extraction, and produce robust estimates of CSG water production at a regional scale, while not relying on excessively complex numerical models or excessive data input requirements.
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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 | 0.003 | 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".