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Record W4408031482 · doi:10.2118/0325-0022-jpt

Is There a Hydraulic Fracturing ‘Blind Spot’ in Conventional Reservoirs?

2025· article· en· W4408031482 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic fracturingBlind spotPetroleum engineeringGeologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Almost all notable advancements in hydraulic fracturing technology over the past 2 decades have stemmed from the rise of the tight-oil and -gas industry. During this time, completions equipment evolved from the analog workhorses of yesteryear into increasingly sophisticated, digitized, and efficient machines that rival anything found in the oilfield technology portfolio. The transformation also includes mountains of scientific research that have enabled engineers and specialists to model and stimulate tight reservoirs with greater confidence and accuracy. But amid all this success, a question has been brewing for years: Can modern fracturing technology be effectively applied beyond the tightest, lowest-quality rocks? Martin Rylance and a team of completions experts from independent oil producer Liberty Resources and modeling firm ResFrac Corp. argue that the industry has developed a “blind spot” when it comes to low—but not ultralow—permeability conventional reservoirs. They believe these formations could be effectively developed using modern fracturing techniques, but they have largely been overlooked. According to Rylance, industry veteran and managing director of IXL Oilfield Consulting, this blind spot exists because North American onshore operators became focused on fracturing hard, tight rock in the 1970s and 1980s with fractured vertical wells. “We skipped a step,” Rylance explained. “The technology for fractured horizontal wells in conventional rock wasn’t in place when we were developing tight-ish rock across North America. That’s now the challenge that the rest of the world faces—there’s no supporting extensive database to draw upon for the development of conventional fractured horizontals.” This gap in knowledge didn’t hinder the expansive development of tight rock across the US and Canada, but it has left a significant opportunity untapped. Rylance and his coauthors lay out their case in two new papers, SPE 223561 and SPE 223562, which support the idea that horizontal multistage wells can outperform fractured vertical wells—perhaps by orders of magnitude in certain cases. Both papers were presented at this year’s SPE Hydraulic Fracturing Technology Conference and Exhibition (HFTC) in the Houston area, where the authors made their case for revisiting low‑quality conventional formations with a new perspective—and carefully selected technology. Rylance spoke to SPE 223561 and highlighted a concept known as the resource triangle, first introduced by a group led by the late hydraulic fracturing pioneer Stephen Holditch. At the top, the triangle illustrates the scarcity of high-quality conventional reservoirs, as opposed to its base where we find a much larger but economically challenging category of tight oil, shale gas, and other unconventional plays that, despite their difficulty, have reshaped global energy markets. “As you move down the triangle, you get more certainty regarding the resource,” said Rylance. “But also increasing cost, decreasing recovery factors, and a growing technology requirement to develop—more energy in, less energy out, typically.” The key takeaway, Rylance said, is that “fracturing is an inevitably” as the industry exhausts the so-called good rock. What remains are long overlooked pay zones that lie between the middle and the bottom of the triangle. “Those familiar with the classics know that Dante’s Inferno has nine levels of misery on the journey to hell—perhaps there is a tenth: hydraulic fracturing. Frac crews globally are at the bottom of this triangle, gleefully awaiting everybody’s arrival, and we need to be there with the right solutions to help them out,” said Rylance.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.248
Teacher spread0.240 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2025
Admission routes1
Has abstractyes

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