Reservoir Engineering for Unconventional Gas Reservoirs: What Do We Have to Consider?
Bibliographic record
Abstract
Abstract The reservoir engineer involved in the development of unconventional gas reservoirs (UGRs) is required to integrate a vast amount of data from disparate sources, and to be familiar with the data collection and assessment. There has been a rapid evolution of technology used to characterize UGR reservoir and hydraulic fracture properties, and there currently are few standardized procedures to be used as guidance. Therefore, more than ever, the reservoir engineer is required to question data sources and have an intimate knowledge of evaluation procedures. We propose a workflow for the optimization of UGR field development to guide discussion of the reservoir engineer's role in the process. Critical issues related to reservoir sample and log analysis, rate-transient and production data analysis, hydraulic and reservoir modeling and economic analysis are raised. Further, we have provided illustrations of each step of the workflow using tight gas examples. Our intent is to provide some guidance for best practices. In addition to reviewing existing methods for reservoir characterization, we introduce new methods for measuring pore size distribution (small-angle neutron scattering), evaluating core-scale heterogeneity, log-core calibration, evaluating core/log data trends to assist with scale-up of core data, and modeling flow-back of reservoir fluids immediately after well stimulation. Our focus in this manuscript is on tight and shale gas reservoirs; reservoir characterization methods for coalbed methane reservoirs have recently been discussed.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".