Geochemical Productivity Index (<i>Igp</i>): An Innovative Way To Identify Potential Zones With Moveable Oil in Shale Reservoirs
Bibliographic record
Abstract
Summary In this paper we present a method for identifying intervals in shale oil reservoirs that contain moveable hydrocarbons with a novel geochemical productivity index (PI), Igp. This index merges three important rock properties that must always be considered for sound shale oil reservoir characterization: vitrinite reflectance (%Ro), oil–saturation index (OSI), and free–water porosity (ϕFW). Integrating this index with other petrophysical properties and geomechanical parameters defines intervals with high moveable oil content. Shale oil is both a source rock and an unconventional reservoir rock. Hence, it is critical to know both its organic–matter (OM) maturity and its oil/water flow capacity. The introduced Igp considered these features simultaneously; maturity was evaluated by discretizing %Ro from 0 to 1, depending on whether the rock was immature or not; free oil flow capacity modeled the normalizing OSI between 0 and 1 on the basis of results from the Rock-Eval VI pyrolysis (REP) obtained in the laboratory or by electric logs; and water flow capacity was estimated from ϕFW, obtained using a nuclear–magnetic–resonance (NMR) log, which was transformed into an index between 0 and 1. Flow oil capacity was defined as the amount of moveable oil that exceeded the sorption capacity of the source rock. Using 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 type of wellbore architecture (i.e., deviated, horizontal, geosteered). Initially, a correlation between vertical depth and %Ro was developed. This resulted in a continuous OM–maturity curve along the well section. Next, OSI was simulated by using a bin porosity from an NMR log, where T2 was between 33 and 80 milliseconds and was correlated with OSI data from REP. As a result, a good match between the simulated and the real OSI data was achieved. Similar to OSI, ϕFW was also calculated from the NMR log, but it used a bin porosity when T2 was greater than 80 milliseconds. These three parameters were then transformed to fractional indices, which were combined into a unique index, Igp. When the index was greater than 0.66, there was a good chance that the three conditions mentioned above would be met. For the example well considered in this study, it was found that almost 30% of the total vertical section had good moveable oil potential. This corresponded to 10 intervals in the well. The key novelty of this paper is that we have developed 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 because of the continuity of the Igp curve, in addition to the Igp integrated criteria that are usually applied independently. The Igp index is a simple–to–use approach. However, because it is a new method, an explorationist should validate it against real oil production information.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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".