Integrated Reservoir Characterization based Workflow for Hydraulic Fracture Simulation
Bibliographic record
Abstract
Summary This paper outlines a novel, integrated workflow to enhance the quality of input data for hydraulic fracture simulations. The methodology employs cross-disciplinary collaboration—merging petrophysics, rock physics, geomechanics, and reservoir engineering—to build static models that accurately represent in-situ reservoir conditions. The process begins with advanced well-log conditioning to derive dependable petrophysical properties, followed by the generation and rigorous quality control of elastic rock parameters essential for geomechanical modeling. These refined datasets serve as the backbone for constructing geomechanical models compatible with leading fracture simulation software. A structured, iterative calibration process aligns model predictions with calibration data thereby constraining uncertainties and non-uniqueness. The workflow also computes additional parameters necessary for fracture simulation ensuring a comprehensive and internally consistent input set. By formalizing this integration and quality control framework, the paper delivers a practical, repeatable approach to generate input data for fracture simulations. It empowers technical professionals to create more reliable models. Even the most advanced simulators require well-constructed, high-fidelity input data to yield meaningful and accurate results.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".