Effect of Completion Heterogeneity in a Horizontal Well With Multiple Fractures on the Long-Term Forecast in Shale-Gas Reservoirs
Bibliographic record
Abstract
Summary Shale gas reservoirs have become a significant source of gas supply in NorthAmerica because of the advancement of drilling and stimulation techniquesenabling commercial development. The most popular method for exploiting shalegas reservoirs today is the use of long horizontal wells completed withmultiple-fracturing stages [multifractured horizontal wells (MFHW)]. Thestimulation process may result in biwing fractures or a complexhydraulic-fracture network. However, there is no method to differentiatebetween these two scenarios with production data analysis alone, makingaccurate forecasting difficult. For simplicity, hydraulic fractures are often considered biwing whenanalyzing production data. A conceptual model that is often used for analyzingMFHWs is that of a homogeneous completion in which all fractures have the samelength. However, fractures of equal length are rarely if ever observed (Ambroseet al. 2011). In this paper, production data from heterogeneous MFHWs (i.e., where allfracture lengths are not the same) is studied for reservoirs with extremely lowpermeability. First, the simplified forecasting method of Nobakht et al.(2012), developed for homogeneous completions, is extended to heterogeneouscompletions. For one specific case, the Arps' decline exponent is correlated tothe heterogeneity of the completion. It is found that, as expected, Arps'decline exponent (used after the end of linear flow) increases with theheterogeneity of the completion. Finally, it is shown that ignoring theheterogeneity of the completion can have a material effect on the long-termforecast. We have assumed planar hydraulic-fracture geometries for our modelling inthis work and discuss the implications of this when more-complex fracturegeometries are created. This seems to be more common in shale gas reservoirs.We provide an example of low-complexity, planar fracture geometries creatednear an MFHW and observed on an image log at an offset well.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".