Fracture Stimulation Challenges in Tight Walloons Coal Measures: Surat Basin Queensland, Australia
Bibliographic record
Abstract
Abstract Walloons Coals of the Surat Basin, Queensland (Australia) contain world class Coal Seam Gas (CSG) plays, where permeability varies from high (>1Darcy), due to Gaussian curvature-related natural fracture connectivity, to low (<1mD) due to unidirectional fracture-systems attributed to regional unidirectional flexure. The low permeability Walloons Coals require stimulation to unlock their gas resources. This contribution describes the design evolution of stimulation concepts in the Surat Basin in context of five key subsurface drivers Coal net to gross: Surat Basin coals contain 30 coal seams with a cumulative thickness of 20-35m in a gross rock column of >300m Permeability of coals requiring stimulation for economic flow rates varies from <1mD - ~30mD Varying stress regimes, both vertically and laterally Ductile rock properties in Walloons coal reservoirs Productivity Index drop (PI drop) can occur when (incompressible) water is replaced by (compressible) gas during coal dewatering Early stimulation treatments in Surat Basin (pre-2010) followed ‘standard’ high rate water/sand designs adapted from the shale industry. However, high treating pressure and rates resulted in several instances of casing shear (Johnson et al. 2003) particularly at depths associated with stress regime transitions. Subsequent designs (2010-12) repeated water fracs albeit including ample diagnostics (Johnson et al 2010; Flottmann et al 2013), showing that water fracs appear to be ineffective in stimulating Walloons Coals. Design optimizations in 2015 (Kirk-Burnnand et al. 2015) based on extensive modeling work (Pandey and Flottmann 2015), identified low rate gel fracs as optimal to stimulate rocks with ‘ductile’ Walloons-specific coal properties. However, treatment rates were limited to optimize height growth, both to connect coals and to avoid height growth into non-reservoir. Initial production data indicated a drop in well productivity in some fracture stimulated coals (Busetti et al. 2017). Consequently, stimulation designs were modified in late 2016 to account for such productivity drops while maximizing the fluid recovery. Early time post stimulation drawdown strategy was also field-tested to mitigate loss of well productivity due to excessive drawdown which could cause partial or full fracture closure (especially near the wellbore region), and lead to loss of communication between reservoir and well. Sub-surface drivers identified in tight Walloons Coals control the effectiveness of any stimulation option deployed. These drivers influence the effectiveness of stimulation in multiple ways. First, these drivers can lead to a sub-optimal connectivity between well and reservoir resulting in poor productivity and marginal recovery. Second, the drivers may influence an operator towards expensive stimulation options which may provide better well to reservoir connectivity but diminish the economic value due to the high costs involved. Hence the inclusion of sub-surface drivers in selecting stimulation design is paramount as demonstrated in this paper.
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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".