Empirical Links Between Sub-Surface Drivers and Engineering Levers for Hydraulic Fracture Treatments and the Implications for Well Performance
Bibliographic record
Abstract
Abstract Does the sub-surface drive completion design or is the rock less of a concern with industry trends to higher proppant-, fluid- and stage-intensities? To address this challenge it was first necessary to understand; 1) how the sub-surface could potentially influence completion and stimulation design, 2) what are the available engineering levers and moreover, 3) whether well performance has actually been impacted by tailoring completions in different plays from specific case-studies. Although there is a multitude of published field examples of how completion design changes have driven value, clarity around the inter-connectedness with sub-surface variability, either between plays or within a play, is commonly missing. New templates have been developed that describe the conceptual links between the nine key 'Sub-surface Drivers' for hydraulic fracturing and their associated engineering Levers categorized by well-, fluid-, proppant- and stage-design. These templates are a compilation of extensive empirical observations from both operations and field performance reviews incorporating thousands of wells across North America, supported with learnings from geomechanical theory and modeling. The nine Sub-surface Drivers that influence completion design and control the access to hydrocarbons are, 1) mobility, 2) reservoir pressure, 3) gross thickness, 4) layering heterogeneity, 5) rock stiffness, 6) natural fractures, 7) stress anisotropy, 8) risk of fraccing faults and, 9) risk of fraccing out of zone. Drivers 1-7 govern the connectivity, whereas 8 and 9 influence stimulation ineffectiveness. It is proposed that there are approximately fifteen primary engineering Levers related to these nine Drivers, which have been shown to have a measurable impact on completion effectiveness and/or production. Case studies illustrate that the Sub-surface Drivers play a significant role in most plays, but they are not all relevant in every play. The challenge is to acknowledge the variability, or lack of, and pursue completion design optimization goals, while managing the variance in the well performance range. Whereas industry trends of increasing completions intensity have delivered more value in many plays, the Sub-surface Drivers concept have primarily proven useful to mitigate against poor wells in development and explain exploration failures. By developing a systematic set of templates for Drivers and their respective levers, learnings from other operators can be high-graded through the formulation of connectivity analogues with the goal of showing where changes in completion design may be more, or less applicable.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".