Pilot Program in Mexico Classifies Oil and Gas Projects Using UN Framework
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 196566, “Mexico Pilot Project To Classify Oil and Gas Projects Using United Nations Framework Classification,” by Satinder Purewal, SPE, Imperial College, and Fidel Juárez Toquero, SPE, and Eduardo Simón Burgos, National Hydrocarbons Commission of Mexico, et al., prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. A pilot project was initiated to classify oil and gas projects in Mexico using the United Nations Framework Classification (UNFC). The UNFC assists in identifying key social and environmental factors that could impede the movement of oil and gas volumes higher up the value chain. To the authors’ knowledge, this is a unique project with significant value-adding outcomes that can be replicated in other countries. Mexico’s Perspective For proper assessment of discovered and undiscovered hydrocarbon volumes, Mexico adopted the Petroleum Resources Management System (PRMS) as its official classification framework. The country features a wide spectrum of cultures, indigenous identities, and social organizations, and in some cases, industry activities could represent a threat to these aspects of Mexican life. As an effect of the nation’s geographical location, a diversity of ecosystems exists, from deserts to regions rich in flora and fauna such as rainforests and wetlands. This reality emphasizes the need for robust legislation guaranteeing the protection of the environment. UNFC The UNFC is a system for classifying resources including petroleum, minerals, and renewables. Quantities are classified on the basis of a 3D, three-axis system (Fig. 1). Each axis assesses different factors on the basis of three fundamental criteria: economic and social viability (E), field project status and feasibility (F), and geological knowledge (G), using a numerical coding system. Categories and subcategories of each axis are the building blocks of the system and are combined in the form of classes. A class is defined by a combination of categories or subcategories sourced from each of the three criteria. A classification made according to the UNFC will be expressed with a three-digit code providing the location of the project in the 3D system, starting with the E, then the F, and finally the G axis. Additionally, the three-digit code can be expressed in terms of categories (E1, F1, G1) which will be read as classes, as well as in terms of subcategories that are read as subclasses. The three-digit code provides information on the maturity status of a project. When category level is used, projects can be classified as Commercial, Potentially Commercial, Noncommercial, Exploration, and Additional Quantities in Place associated with known and potential deposits. When the subcategory level is used, projects can be subclassified. The relationship between the PRMS and the UNFC can be explained better in terms of the linkage between the evaluation processes of the range of uncertainty and chance of commerciality in both systems. The PRMS categorizes the volumes to size the range of uncertainty present in the estimates, while the UNFC does the same through G-axis evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".