Acoustic Data Driven Application of Principal Component Multivariate Regression Analysis in the Development of Unconfined Compressive Strength Prediction Models for Shale Gas Reservoirs
Bibliographic record
Abstract
Abstract Unconfined compressive strength (UCS) equally represented as geomechanical strength remains a critical mechanical property in the successful implementation of key technologies for shale gas reservoirs’ development and production. Attention has been less concentrated on prediction models’ development for shale geomechanical strength evaluation. Majority of the existing shale geomechanical strength correlations are dependent on single log input parameter, which is insufficient to account for the complex and non-linear behaviour of UCS across the entire reservoir interval of interest. The high relevance of UCS has therefore triggered the need for the application of an integrated system of principal component – multivariate regression analysis in driving UCS predictive models’ development for shale gas reservoirs. Generated acoustic datasets of notable shale gas reservoirs (Marcellus, Montney, Longmaxi and Roseneath) in respective countries (United States of America (USA), Canada, China and Australia) were used. Statistical test analysis was conducted in validation for wider applications of the developed UCS prediction models. Models development were driven by 21,708 datapoints of acoustic parameters, models’ accuracy ratings were above 99%, R-squared values had high degrees of closeness to unity, mean absolute percentage error (MAPE) values were at less than 10% and coefficient of variation (COV) at less than (1.0). UCS prediction models were all dependent on multiple direct log measured acoustic parameters in distinction to existing UCS empirical correlations; thus, a pure reflection of significant boost to the accuracy and reliability of UCS measurements for shale gas reservoirs. The developed prediction models will promote geomechanical strength accountability and lead to creation of a robust base in minimization of wellbore instability problems, optimization of wellbore trajectory and containment of hydraulic fractures. This will significantly contribute in putting gas resources of shale reservoirs with enormous potentials, at the forefront of quantitatively meeting natural gas requirements in global energy demand.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".