Lessons Learned from History-Matching the First Out-Of-Sequence Fracturing Field Test in North America
Bibliographic record
Abstract
Abstract One of the considerations in Out-Of-Sequence Fracturing treatment is maximizing reservoir contact by creating fracture complexity through reducing or possibly eliminating or neutralizing the in-situ stress anisotropy (differential stress) to enhance hydraulic fracture conductivity and connectivity by activating planes of weakness (natural fractures, fissures, faults, cleats, etc.) within the formation in order to create secondary or branch fractures (induced stress-relief fractures) and connect them to the main bi-wing hydraulic fractures. In Out-Of-Sequence Fracturing, this is achieved by beginning fracturing Stage 1 at the toe of the well and then moving toward the heel and fracturing Stage 3 so that there is a degree of interference between the two fractures followed by placing Stage 2 between the previously fractured Stages 1 and 3. Out-Of-Sequence Fracturing in this mode ensures that fracture in Stage 2 (Centre Frac) takes advantage of the altered stress in the rock and connects to the stress-relief fractures from the previous Stages 1 and 3 (Outside Fracs), thus enhancing the connectivity and conductivity of the fracture network. Out-Of-Sequence Fracturing has already been tested successfully by LUKOIL Group in treating eight wells in Western Siberia in 2014. The first case of Out-Of-Sequence Fracturing in North America was later conducted in Western Canada in 2017. In this work, a three-dimensional hydraulic fracture extension simulator is rigorously calibrated by history-matching the observed treatment pressures and instantaneous shut-in pressures (ISIP) from the Out-Of-Sequence Fracturing field treatment in Western Canada in order to reliably quantify effective fracture geometries. Then, a separate set of fracture modeling is conducted to predict hydraulic fracture geometries in a conventional (Sequential Fracturing) treatment of the same candidate well. Finally, production forecasting is used to assess the production potential from the candidate well based on each set of the generated fracture geometries from each of the scenarios (Out-Of-Sequence Fracturing versus conventional Sequential Fracturing). The results of coupling rigorously calibrated fracture modeling and production forecasting indicate noticeable production uplift potential from the carefully designed Out-Of-Sequence Fracturing, with the realization that its success is sensitive to both treatment variables (stage spacing, well placement, treatment fluid viscosity and rate, and Centre Frac proppant size and tonnage) and formation's petrophysical and geomechanical properties (magnitude of stress anisotropy, Young's modulus, Poisson's ratio, process zone stress/net extension pressure, fracturing gradient, and matrix permeability). A carefully designed Out-Of-Sequence Fracturing should avoid excessive fracture complexity that impedes fracture growth due to pressure-out and screenout. This work is the first attempt in comparative evaluation of the impact of Out-Of-Sequence Fracturing incorporating the actual field data into fracture modeling coupled with production forecasting. The learnings from this multi-faceted study are worth sharing with the industry and could be used to guide future successful designs of the Out-Of-Sequence Fracturing for completion optimization in both unconventional and conventional reservoirs. From a large-scale field-development perspective, when conducted in multiple wells, optimized Out-Of-Sequence Fracturing has the potential of rendering full-length interference effect and 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".