Models for Calculating Organic and Inorganic Porosities in Shale Oil Reservoirs
Bibliographic record
Abstract
Abstract The objective of this study is to present methods for calculating organic and inorganic porosities in shale oil reservoirs. This is achieved by combining density, neutron and NMR logs as well as laboratory geochemical and synthetic geochemical properties of organic matter. The study also presents methods for calculating these porosities when all the above data are not available. This is important as data scarcity is a common problem in most shale reservoirs. Shales are generally composed by clays, inorganic matrix, organic matter and natural fractures. In this study, responses of density, neutron, and NMR logs are written in terms of properties of each shale component including clays, solid and porous volume for both inorganic (including natural fractures) and organic matter. Different analytical models are built depending on available input data and the approach used to convert weight total organic carbon (TOC) to TOC volume percentage. However, as is usually the case, the availability of different sources of information including geochemical data, routine and/or special core analysis will enhance the validity of the interpretation. Models developed in this study indicate that organic porosity results (intrinsic and scaled to total volume) are very consistent with values measured in the laboratory and values reported in the literature. There are three approaches for converting weight TOC to percent volume TOC. Our results show that these three approaches have to be used carefully. Their indiscriminate use can lead to errors as the organic porosity is very sensitive to the TOC transformation. The organic porosity is also very sensitive to properties assumed for each component of the reservoir rock. Depending on petrophysical and reservoir engineering needs, the organic porosity can be easily scaled to the volume of only the organic matter (intrinsic organic porosity) or to the bulk volume (total organic porosity) of the total system. In addition to organic porosity, the models developed in this study also allow calculating kerogen volume and its respective solid portion, allowing thus an estimate of solid kerogen and porosity within the kerogen material. Furthermore, the models also allow calculating inorganic porosity (matrix plus natural fractures). Unlike current models that use separately conventional logs or NMR logs to calculate the porosity associated with organic matter, this study integrates all these logs as well as laboratory and synthetic geochemical properties of organic matter to develop new methods for estimating rigorously-scaled organic porosity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".