Use of Probabilistic Methods to Assess a Portfolio of Conventional and Unconventional Resources
Bibliographic record
Abstract
Abstract Recoverable hydrocarbon resource assessments underpin decision making and business planning in the oil and gas industry. Understanding the uncertainty associated with the resource assessments are key to sound decisions that are robust against low or high outcomes. This paper outlines a probabilistic approach to resource assessment in order to characterise resource uncertainty in a portfolio containing primarily Coal Seam Gas resources. The Probabilistic Resource Assessment (PRA) process outlined in this paper allows calculation of risked and unrisked probabilistically derived commercially recoverable resources at a field or permit level as well as at a portfolio level. This process incorporates Undiscovered ("Prospective") resources and Contingent Resources as well as resources that are producing or are under development. The key steps in this process include: definition of input distributions, probabilistic calculation of technically recoverable resources at a field level, estimation of economic chance of success, probabilistic estimate of commercially recoverable resource and aggregation of resources to a portfolio level. This process has been applied within an integrated joint venture supplying Liquefied Natural Gas (LNG) and domestic gas markets. The process has been used primarily to understand the uncertainty range of the total resource as well as the production profile within the upstream portfolio. Sensitivities to product prices or development costs can be investigated to enable a deep understanding of the key drivers and variables of the resource assessment. Various methods for determining recoverable hydrocarbon resources have been well documented. Broadly speaking, these methods can be categorised as probabilistic methods and deterministic methods. Typically, unconventional resources are assessed using deterministic methods. The process presented here is a robust probabilistic approach to determine a risked view of recoverable resources within an entire portfolio including both unconventional and conventional resources.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".