Geochemical Productivity Index, Igp: An Innovative Way to Identify Potential Zones with Moveable Oil in Shale Reservoirs
Bibliographic record
Abstract
Abstract A method is presented to identify intervals in shale oil reservoirs that contain moveable hydrocarbons with a novel geochemical productivity index, Igp. This index merges 3 important rock properties that always have to be considered for sound shale oil reservoir characterization: vitrinite reflectance (%R°), oil saturation index (OSI), and free water porosity (ϕFW). Integration of this index with other petrophysical properties and geomechanical parameters leads to define intervals with moveable oil. Shale oil is both source and reservoir rock. Hence, it is critical to know both its organic matter maturity and oil/water flow capacity. The introduced Igp considers these features simultaneously; maturity is evaluated by discretizing %Ro from 0 to 1 depending whether the rock is immature or not; free oil flow capacity is modeled normalizing OSI between 0 and 1 based on results from Rock-Eval pyrolysis (REP) obtained in the laboratory or electric logs; and water flow capacity is estimated from ϕFW, obtained with the use of NMR log, which is transformed to an index between 0 and 1. Use of the Igp is explained with real data from a vertical well that penetrates several stacked shale oil reservoirs. However, the same approach can be used in any other kind of wellbore architecture (deviated, horizontal, geosteered). Initially, a correlation between vertical depth and %R° is developed. This results in a continuous organic matter maturity curve along the well section. Next, OSI is simulated with the use of a bin porosity from NMR log, where T2 is between 33 and 80 ms and is correlated with OSI data from REP. As a result, a good match between simulated and real OSI data is achieved. Similar to OSI, ϕFW is also calculated from the NMR log but using a bin porosity when T2 is greater than 80 ms. All these 3 parameters are transformed to partial indexes, which are combined into a unique index, Igp. When the index is greater than 0.66 there is a good chance that the 3 conditions mentioned above will be met. For the example well considered in this study, it was found that almost 30% of total vertical section has good moveable oil potential. This corresponds to 10 intervals in the well. The key novelty of the paper is that it develops a continuous curve of an index that is easy-to-use and is powerful for identifying intervals with moveable hydrocarbon potential. This is true even in those intervals without laboratory data due to the continuity of the Igp curve. In addition, the Igp integrates criteria that are usually applied independently.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".