A Convenient Technology to Calculate Geomechanical Properties From Drilling Data
Bibliographic record
Abstract
Abstract The multistage hydraulic fracturing is the best practice to stimulate unconventional hydrocarbon reservoirs for optimal production. Recent studies suggested that selective stimulation design could significantly increase production rates at a reduced cost rather than using non-selective geometric stages. An optimal design needs detailed logging and core information to selectively perforate and optimize the stimulation treatment. In most cases, the non-selective evenly spaced geometric stimulation design is used, primarily due to the time consuming and expensive conventional logging tools and techniques. In this article, a 3D wellbore friction model is used to estimate the effective downhole weight on bit (DWOB) from the drilling data, directional survey data and drill string information. The estimated DWOB is used as an input to the inverted rate of penetration (ROP) model along with other drilling data, drill bit specifications and reservoir specific formation constants, to calculate rock mechanical and reservoir properties including, compressive strength, Young’s modulus, porosity, permeability and Poisson’s ratio without the use of expensive downhole logging tools. The rock brittleness index is calculated from the relationship between Young’s modulus and Poisson’s ratio based on the definitions of rock brittleness used in recent years. The field data from horizontal drilling of three sample wells were used to investigate the geomechanical properties in the Montney shale formation and the lower Eagle Ford formation in North America. The calculated geomechanical properties were compared to the corresponding test analysis on cores. The authors investigated the rock brittleness index from the sample well data drilled horizontally in the lower Eagle Ford formation. This novel technology could help geologists and reservoir engineers better exploit unconventional reservoirs leading to optimal selective stimulations and greater net present value (NPV).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".