Integration of Microseismic and Other Post-Fracture Surveillance With Production Analysis: A Tight Gas Study
Bibliographic record
Abstract
This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 131786, ’Integration of Microseismic and Other Post-Fracture Surveillance With Production Analysis: A Tight Gas Study,’ by C.R. Clarkson, SPE, University of Calgary, and J.J. Beierle, SPE, Talisman Energy, prepared for the 2010 SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, 23-25 February. The paper has not been peer reviewed. Quantitative production analysis of tight gas reservoirs is a challenge because of complex reservoir characteristics, induced-hydraulic-fracture properties in vertical wells, operational complexities, and data quality. These challenges make extracting reservoir and hydraulic-fracture properties (i.e., fracture half-length, xf, and fracture conductivity) solely from production and flowing-pressure data difficult, often resulting in nonunique answers. Many tight gas reservoirs are exploited with horizontal wells, often stimulated with multiple hydraulic fractures, imparting greater complexity to the analysis. Flow-regime identification, which is critical to correct analysis, becomes more complicated because of the variety of flow regimes that could be encountered in such wells. Introduction Development of tight gas, shale gas, and coalbed methane (collectively referred to as unconventional gas reservoirs) benefits from advances in drilling, completions, and stimulation technology; formation evaluation; and during-/post-stimulation-surveillance technology. Formation-evaluation techniques enable determining critical parameters such as matrix permeability in ultratight rock from cores, and adsorbed- and free-gas content in shale and coalbed methane from cores and cuttings. During-/post-fracture-stimulation-surveillance technology (such as microseismic monitoring) aids identifying the hydraulic-fracture geometry created in unconventional reservoirs (hydraulic-fracture growth), particularly in coals and shales. Predicting hydraulic-fracture geometries is complicated by heterogeneities, such as natural fractures (healed or open) and layering (with associated contrasts in mechanical properties), and in some cases, by nonlinear elastic behavior. Advanced production-analysis techniques, such as production type curves, supplement reservoir and stimulation information obtained from pressure-transient analysis (well testing) in conventional oil and gas reservoirs and even some unconventional reservoirs such as coalbed methane and tight gas. However, applying these methods to tight gas and shale reservoirs that are produced through multifractured horizontal wells has been difficult because of the complexity of the system, poor quality of flowing-pressure and rate data, and the lack of sufficient data to characterize the system fully. Even if the production and flowing-pressure data were of sufficient quality to identify flow regimes, as in pressure-transient analysis, without additional surveillance information, it is difficult to ascertain how the flow regimes relate to the reservoir and hydraulic-fracture system. If the flow regimes are misinterpreted, then the extracted information will be incorrect. A workflow is proposed to improve the quality of information extracted from production-data analysis (PDA) of hydraulically fractured horizontal wells completed in tight gas reservoirs.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".