Implications of Recent Research into the Application of Graded Particles or Micro-Proppants for Coal Seam Gas and Shale Hydraulic Fracturing
Bibliographic record
Abstract
Abstract Low permeability, naturally fractured reservoirs such as coal seam gas (CSG, coalbed methane or CBM) and shale gas reservoirs generally require well stimulation to achieve economic production rates. Coupling hydraulic fracturing and micro-proppant or graded particle injections (GPI) can be a means to maximise hydrocarbon recovery from these tight, naturally fractured reservoirs, by maintaining or improving cleat or natural fracture conductivity. This paper presents a summary of the National Energy Resources Australia (NERA) project "Converting tight contingent CSG resources: Application of graded particle injection in CSG stimulation" - which assessed the application of micro-proppants, providing guidance on key considerations for GPI application to CSG reservoirs. Over the last decade, laboratory research and modelling have shown the benefits of the application of GPI to keep pre-existing natural fractures and induced fractures open during production of coal reservoirs with pressure dependent permeability (PDP). Laboratory studies, within this study, provide further insight on potential mechanisms and key factors, including proppant size and optimum concentration, which contribute to the success of a micro-proppant placement. Accompanying numerical modelling studies will be presented that describe the likely fluidized behaviour of micro-proppants (e.g., straining models, electrostatic effects, and ‘screen out’ prediction). This paper outlines the necessary reservoir characterization, treatment considerations, and key numerical modelling inputs necessary for the design, execution, and evaluation of GPI treatments, whether performed standalone or in conjunction with hydraulic fracturing treatments. It also provides insight on the practical application of GPI efficiently into fracturing operations, minimizing natural and hydraulic fracturing damage effects, thereby maximizing potential production enhancement for coals, shales and other tight, naturally fractured reservoirs exhibiting pressure-dependent permeability effects.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".