Preliminary Considerations on the Application of Out-Of-Sequence Multi-Stage Pinpoint Fracturing
Bibliographic record
Abstract
Abstract Out-Of-Sequence Pinpoint Fracturing is a conceptual way of maximizing reservoir contact by creating fracture complexity via reducing or neutralizing the stress anisotropy to improve fracture conductivity and connectivity. A complex fracture network is formed by activating planes of weakness present in the form of natural fractures, fissures, faults, joints, and cleats. Branch fractures are created due to the induced stress-relief fractures where they can be connected to the main bi-wing hydraulic fractures to improve fracture network connectivity. Out-Of-Sequence Fracturing is initiated by fracturing Stage 1 (at the toe) and then fracturing Stage 3 toward the heel. Once a desired degree of stress interference between the Stages 1 and 3 (Outside Fracs) is established, Stage 2 (Centre Frac) is placed between the Outside Fracs. The Centre Frac enhances fracture network connectivity and conductivity by connecting to stress-relief fractures from the outside Fracs, taking advantage of the altered stress state. Out-Of-Sequence Fracturing has successfully been tested in Western Siberia (by LUKOIL) and Western Canada in 2014 and 2017, respectively. A fracture model is calibrated using treatment pressures and instantaneous shut-in pressures (ISIP) from the Out-Of-Sequence Pinpoint Fracturing in Western Canada. The fracture model is coupled with reservoir simulation and RTA to evaluate the production potential in Out-Of-Sequence Pinpoint Fracturing and to conduct an extensive sensitivity analysis on petrophysical/geomechanical properties (stress anisotropy, Young's modulus, Poisson's ratio, process zone stress (PZS)/net extension pressure, fracturing gradient, and matrix permeability) and treatment variables (stage spacing, treatment fluid volume/viscosity/rate, and proppant tonnage/size/concentration) to identify the factors that are most critical to optimizing the treatment. The results reveal noticeable production uplift from a carefully designed Out-Of-Sequence Pinpoint Fracturing, which avoids excessive fracture complexity that impedes fracture growth due to pressureout and screenout. Out-Of-Sequence Pinpoint Fracturing is most sensitive to stage spacing, treatment rate, proppant and fluid intensity for the Centre Frac, stress anisotropy, PZS, and brittleness factor (combination of Young's modulus and Poisson's ratio). Screening of these parameters helps identifying well candidates and treatment strategies to avoid both insufficient fracture complexity and excessive fracture complexity, where higher-than-anticipated treatment pressures are observed as an evidence of shear fractures being filled with treatment fluid, causing an additional component of stress that must be opposed by treatment fluid, realizing that higher pressures are only a risk when they are higher than surface pressure constraints. This is the first attempt in pressure history-matching and screening for formation properties and treatment strategies for maximizing Out-Of-Sequence Pinpoint Fracturing benefits. The learnings from this multi-faceted study guide future successful designs of the Out-Of-Sequence Fracturing for completion optimization in unconventional and conventional reservoirs. Rendering a full-length interference effect is possible by conducting an optimized Out-Of-Sequence Fracturing in multiple wells (as part of large-scale field developments) to ensure optimizing the stress shadowing while reducing the risk of well bashing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".