Frac Hit Induced Production Losses: Evaluating Root Causes, Damage Location, Possible Prevention Methods and Success of Remedial Treatments
Bibliographic record
Abstract
Abstract Frac hits or "frac bashing" is a fracture-initiated well-to-well communication event that can create production losses (or gains), and on occasion, mechanical damage when frac energy from a stimulated well extends into the drainage area or directly contacts an adjacent or offset well. Pressure increases have been detected in wells at distances ranging from hundreds to thousands of feet from the stimulated well. While these in-zone frac hit events do not pose an environmental problem if there is no failure of containment, there can be some alteration of the production potential in one or both of the wells involved. Frac hits along the preferential fracture plane were an uncommon but known event when the completion method only involved vertical wells, but the rate of incidence has increased sharply as the preferred completion method has shifted to relatively closely-spaced, multiple fractured horizontal wells (MFHW) in low permeability formations such as the mudstone rocks commonly referred to as shales. Mechanical damage within the well and success of methods of prevention, damage control and remediation will be examined by case histories and published contexts of incidents in several basins, but will not be the main goal of the paper. The primary effort will focus on examining causes of production loss and duration of the loss, including looking at production declines pre-hit and post-hit. Known causes include in-situ stress alteration potential, timing of fracture closure, near-wellbore proppant loss, liquid loading, rock-fluid interactions, sludges and wetting factors. Also considered will be geological effects such as regional fractures and linked natural fracture clusters. A main objective will be to identify pressure transient, chemical analysis or other monitoring techniques to identify location and type of damage. Remedial operations are most effective when the potential cause of production losses can be ranked probabilistically and the depth of the production-reducing event can be estimated as near-field or far-field. Analyzing this data will also assist in defining whether chemical or mechanical treatments such as refracturing or a hybrid treatment system may be the best approach.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".