Guidelines for the Handling of Natural Fractures and Faults in Hydraulically Stimulated Resource Plays
Bibliographic record
Abstract
Abstract Do fractures help or hinder production in hydraulically stimulated resource plays? Most say they help, but some say they hinder. After monitoring productivity for over 10 years in a number of plays in Shell's unconventional portfolio, it appears that the evidence for fractures helping is more conceptual than empirical, further substantiated by a detailed literature review. The objective of this paper therefore is to bring some objectivity to the discussion around the impact of structure using logical arguments by reason, incorporating knowledge of the variability in structure and well performance within the spectrum of unconventional plays. Fundamental to this assessment is the recognition that different scales of features will have a markedly different impact. And to communicate the concepts herein, small-scale features are referred to as "natural fractures" and large-scale features, referred to as "faults" or "lineaments". This analysis indicates that the variability in (small-scale) natural fracture intensity across most plays is not sufficient to be detected in well performance metrics, given the other sub-surface heterogeneity and the large range in estimated ultimate recovery (EUR) for any given set of wells. Furthermore, natural fracture connectivity is typically low and stimulation of networks is not supported by data or trials. It is proposed to consider natural fractures as an intrinsic rock property which will modify the bulk geomechanical properties of the formation. The only exception found was for folded tight-sand plays, where fracture network connectivity may be sufficient to provide a measurable enhanced deliverability. Understanding the impact of seismically-visible, planar, structural features (e.g. faults or lineaments) proved to be more problematic, with operators reporting both EUR increases and decreases. This inconsistency is explained with a novel concept classifying faults as contained or uncontained, contingent on whether they are within a closed fluid- and pressure-contained system, or not (respectively) before and after hydraulic stimulation. Rather than searching for a production performance correlation, it is suggested that an enhanced understanding of the physical processes during a hydraulic stimulation would be more beneficial to clarify the impact of structure. And to this aim, a compilation of potential fracturing diagnostics is presented herein.
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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 | 0.066 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".