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Record W2923190916 · doi:10.2118/0419-0027-jpt

To Solve Frac Hits, Unconventional Engineering Must Revolve Around Them

2019· article· en· W2923190916 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil shaleHydraulic fracturingDrillingEngineeringSession (web analytics)Order (exchange)BusinessMining engineeringGeologyPetroleum engineeringFinanceAdvertisingMechanical engineering

Abstract

fetched live from OpenAlex

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.”

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)0.006
metaresearch head score (Gemma)0.018
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 score0.034
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.009
Scholarly communication0.0090.011
Open science0.0030.006
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0340.011

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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