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Record W2904969780 · doi:10.2118/1218-0034-jpt

Fighting Water With Water: How Engineers are Turning the Tides on Frac Hits

2018· article· en· W2904969780 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWellheadWorkoverEngineeringInfillOil shaleProduction (economics)Petroleum engineeringGeologyManagementCivil engineeringEconomicsWaste management

Abstract

fetched live from OpenAlex

Frac hits were once a painful cost of doing business for Abraxas Petroleum. But today, the San Antonio, Texas-based shale producer has softened the blows dealt by this widespread and challenging problem. Its approach, called “active well defense,” has been put to the test amid the rolling hills of the company’s North Fork oil field in McKenzie, North Dakota. “Necessity is the mother of invention—and that’s our story here,” said Peter Bommer, vice president of engineering at Abraxas, who noted that the driver of its strategy was not production declines, as it has been for others. Instead, active well defense is designed to prevent temporary, yet costly, production stoppages caused by unabated frac hits filling parent wells with sand. The company starts by injecting produced water at low pressures into older, parent wells. It flows down miles of wellbore and into the formation’s partially depleted fracture networks where it has proven to prevent the damage often caused by high-pressure hydraulic fracturing of new child wells—known as frac hits. Active well defense relies on sporadic injections to reinforce the preloaded water. To do this, Abraxas has partnered with a technology vendor Abra Controls, whose custom communications network gives engineers “real-time pressure monitoring so we can watch the wellhead pressures in the parents, and when we start to see interference, we start pushing back on it,” Bommer said. Active well defense is now used across Abraxas’ infill program in its 3,300-acre development targeting the Bakken and Three Forks shale plays. Its genesis and evolution are covered in two technical papers, the latest of which was published at the start of the year (SPE 184851 and SPE 189860). Renewed Trend Examples of similar strategies have recently emerged from other major North American plays. They include BHP Billiton’s “preloads” in the Eagle Ford Shale of Texas and Canbriam Energy’s “pump-ins” in the Montney Shale of Alberta. Similar tactics are being tested in the Permian Basin and the Anadarko Basin. This trend sits on one side of the well-defense spectrum, while on the other are the more capital-intensive projects known as cube developments that drill and complete a dozen or more wells in quick succession to avoid the downsides of reservoir depletion. Published results on injection-based well defense are encouraging, but it remains unclear whether they will scale up to stem concerns over the shale sector’s ability to sustain record output in the face of falling productivity of new child wells. These operations are distinct from refracturing, as both Abraxas and BHP emphasize that the injections use pressures that do not open new fractures, or affect existing ones much. Injection-based defense is not stopping frac hits, said Marcus Bayne, a fracturing supervisor with Abraxas. “But if we can interfere with the interference, then we can have the fluid we’re injecting act as a shock absorber, or a diffuser, so we don’t push solid material into the parent wellbore.”

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0210.011
Scholarly communication0.0190.015
Open science0.0020.008
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0190.008

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.

Opus teacher head0.004
GPT teacher head0.180
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes1
Has abstractyes

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