To Solve Frac Hits, Unconventional Engineering Must Revolve Around Them
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Bibliographic record
Abstract
Hydraulic fracturing as it is now executed in North American shale plays may have to be replaced with a much more intricate scheme—one that better matches the complex fabric of tight reservoirs. The impetus for an engineering overhaul is being forced by the prevalent well-to-well fracture interactions known as frac hits. These events are the subject of intensifying study by US and Canadian shale producers that have attributed them to lowering oil recovery factors from new child wells by 20–40% while inflicting even higher losses on older, yet less productive, parent wells. How the industry manages the impact of frac hits going forward is likely to define its future growth rate. “They’re not going away—we’re only going to be drilling more and more child wells,” said Brendan Elliot, a senior completions engineer with Devon Energy, as he advised a large gathering of petrotechnical colleagues that they need to “quantify the risk” of frac hits as early as possible in order to know what to do next. Elliot provided a rare look into how a large shale operator is dealing with the problem during the opening session of SPE’s Hydraulic Fracturing Technology Conference (HFTC) held recently in The Woodlands, Texas. His outline highlighted the extent to which the Oklahoma City-based producer is revolving its infill development programs around frac hits. “The things you really want to look at are where the positive and negative events are occurring,” he explained while showing an analysis program that Devon has spent the past couple of years building to visualize those outcomes. The tool, which analyzes data from more than 2,000 frac hit events, has led the company to realize that what determines whether a frac hit will benefit or hurt production comes down to time and production volumes. While this relationship was not a new learning, Devon has quantified it to 10 months and 100,000 bbl. Once both limits have been surpassed, the expectation is that frac hits will have negative consequences on pad production. Elliot elaborated on how this trend is acted upon using the company’s three-pronged approach. Its component pieces include the design of infill wells, addressing reservoir depletion through pressure management (e.g., repressurizing tactics), and then zooming out to adjust the wider field development program. The sum of these parts is a new playbook for unconventional completions, one that stands in stark contrast to the “cookie cutter” designs that gave rise to the shale revolution. Its methodical approach to decision making is aimed at gaining control over fracture growth and maximizing production from child wells. This can be done by adjusting fluid rates in a single well scenario, or altering an entire field development plan based on the risk that frac hits pose. In just a few slides, Elliot succinctly linked together years of accumulated knowledge into a workflow that could feasibly be replicated by other companies struggling with the sector’s most pressing reservoir management issue. “These full-life cycle workflows—don’t get me wrong—these will be lengthy processes, and large projects, so we need to improve as an industry,” cautioned Elliot. “Every mitigation strategy has a risk, it’s going to be specific to each reservoir, it’s going to be variable in the black oil window and your volatile gas window, and you really must cater to the asset value and the value to protect [the resource] in place.”
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it