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Record W2954535201 · doi:10.2118/191644-pa

Geochemical Productivity Index (<i>Igp</i>): An Innovative Way To Identify Potential Zones With Moveable Oil in Shale Reservoirs

2019· article· en· W2954535201 on OpenAlexaff
Jaime Piedrahita, Roberto Aguilera

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersEcopetrol
KeywordsOil shalePetrophysicsPetroleum engineeringGeologyMaturity (psychological)Tight oilSoil sciencePorosityMineralogyGeotechnical engineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.016
GPT teacher head0.272
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Citations4
Published2019
Admission routes1
Has abstractyes

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