Deriving 3D Model of Reservoir Geomechanical Properties Through an Integrated Geostatistical and Basin Modeling Approach - An Example from a Mature Field in Saskatchewan, Western Canada
Bibliographic record
Abstract
Abstract Measurement of geomechanical properties using seismic and laboratory methods have been used in oil and gas industry for several years. Laboratory methods, in most cases, take only small samples from consolidated rocks, which may not be representative of the elastic regime existing in the reservoir owing to sample size. In general, geomechanical studies are performed on a well-by-well basis. Measurements calculated at the wellsite are then used as calibration points to convert the 3-D seismic data to geomechanical cube. However, elastic properties measured this way are restricted to the well location and cannot be interpolated across the reservoir. To overcome these challenges, this paper describes an approach for deriving and discretizing geomechanical and other elastic properties in the reservoir by integrating results of 3D geo-cellular and basin models. The workflow presented in this paper is utilized to calculate cell-by-cell elastic properties of the reservoir by integrating parameters from both basin model and 3D geo-cellular grid. The basin model reconstructs the geologic history (i.e. burial history) by back-stripping the reservoir to its original depositional thickness. Through this reconstruction, the mechanical compaction, pore pressures, effective stress, and porosity-vs-depth relationships are established for the reservoirs. In the final stage, dicretized calculations of geomechanical properties are assigned to each lithotype (facies) in the geomodel. The discretization of elastic properties into 3D grids resulted in better understanding of the prevailing rock elastic properties and stress regimes helping hydraulic fracturing operators in the effective design of their depletion strategies with minimal drilling risks and costs. This approach provides an innovative way of determining effective minimum horizontal stress for the entire reservoir through distribution of elastic properties in a 3D grid. The conventional approach of using small sample plugs is not sufficient to describe elastic properties for an entire reservoir and can be replaced by current approach.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".