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Record W3090275667 · doi:10.2118/0920-0030-jpt

New Fracture Diagnostic Test Delivers Tight Reservoir Data in 2 Hours or Less, Bolsters Future of Engineered Completions

2020· article· en· W3090275667 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringCompletion (oil and gas wells)Scale (ratio)Production (economics)Tight gasFracture (geology)Computer scienceEngineeringOperations managementHydraulic fracturingEconomicsGeotechnical engineering

Abstract

fetched live from OpenAlex

One of the grand challenges facing the oil and gas industry’s global quest to improve the potential of tight reservoirs involves designing horizontal-well completions that match the often-complex heterogeneity of the target formations. This sums up the general concept most call the “engineered completion.” In theory, tailoring a stimulation job to the varying rock properties found along the lateral section of a horizontal wellbore should result in better production for less capital. In practice, the industry has never attempted to do this on a meaningful scale. Among other reasons, the detailed subsurface data required to shake the bonds of geometric, or “cookie cutter,” designs have long been considered too costly to gather or too time-consuming to analyze. A shift is underway though. This is thanks in part to a number of innovations that have hit the market in recent years to meet rising demand for real-time completions-monitoring services. By interpreting pressure signals and other reservoir responses, the new tools aim to give operators the competency they need to adjust fracture designs as the stimulation progresses. But there is another route to the engineered completion. It begins before the well is hydraulically fractured. This effort to predetermine different fracture-stage designs has established its own distinct arena of innovation. Behnam Zanganeh, a former research student at the University of Calgary, was part of a team that developed one of the latest approaches. It is notable in part because it requires no new technology - just new ways of thinking about some old ideas. In a paper published last month during the virtual Unconventional Resources Technology Conference, Zanganeh and his fellow petrotechnical researchers showed how multiple points in a wellbore can be tested for different reservoir-quality parameters quickly and simply relative to traditional methods (URTeC 2838). This is done by combining and fundamentally altering a test known for being slow: the diagnostic fracture injection test (DFIT). The biggest change it calls for is to start flowback after the injection cycle for flowback analysis (FBA), thus eliminating the lengthy falloff cycle associated with the DFIT. The paper outlines a validation study done with Sydney-based Origin Energy which used the blended DFIT-FBA method in a vertical open hole to test two previously untested horizons in the Beetaloo Basin, a frontier unconventional gas play in the center of Australia’s Northern Territory. The results were integrated into the models Origin ultimately used to select its landing target for its subsequent horizontal program.

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)0.005
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.005

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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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