Using Stochastic Simulation To Quantify Risk and Uncertainty in Shale Gas Prospecting and Development
Bibliographic record
Abstract
Abstract Ultra-low permeability shale gas reservoirs have emerged as a significant source of natural gas in North America. Improved drilling, completion and stimulation techniques combined with declining conventional gas reserves have made shale gas a desirable commodity with significant long-term production potential. Despite extensive development and production (particularly in North America), minimal work has been done to develop tools and methodologies for shale gas prospect analysis. Due to the complexity and large extent of unconventional natural gas resources, it is crucial to be able to investigate potential prospects in a methodical manner to determine whether a given prospect has commercial potential and to compare it to other potential prospects. Experience has shown that conventional exploration techniques using deterministic solutions are not suitable for unconventional prospects due to the unique nature of each prospect and the complexity of each reservoir. The most common method for exploiting shale gas reservoirs is through the use of multi-fractured horizontal wells; the resulting well performance, influenced by both the stimulation treatment and complex reservoir attributes, precludes the use of traditional techniques for production data analysis and forecasting. Several new techniques have been developed to improve the quality and efficiency of analysis while accounting for properties that are unique to shale gas (i.e. adsorbed gas in self-sourced reservoirs and nanodarcy level permeability). Also, due to the complexity of shale gas reservoirs, many authors have suggested that deterministic analysis is unsuitable and that probabilistic analysis should be used to quantify the risk and uncertainty associated with shale gas prospects and the associated data. This paper discusses a new tool that was developed specifically for shale gas prospect screening. This tool combines the latest production data analysis and forecasting techniques with a simple, yet rigorous method for stochastically comparing shale gas prospects. The paper discusses the production analysis and rate forecasting techniques used in the tool, as well as the tool development and application. A sample case using simulated data is presented for proof of concept and a discussion is given for extension of the tool for comparison of several potential prospects.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".