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Record W2739385093 · doi:10.2118/0717-0042-jpt

Slug-Smoothing Technology Sees Over 200 Shale Installs, Gets Boost From Schlumberger JV

2017· article· en· W2739385093 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsArtificial liftNonStopGas liftLift (data mining)SluggingJoint ventureGeologyEngineeringBusinessOperations managementComputer sciencePetroleum engineeringEconomicsCommerceManagement

Abstract

fetched live from OpenAlex

The undulating trajectories common to horizontal wells are the source of one of their greatest pain points—gas slugs. When these fast-moving accumulations reach the internals of an artificial lift system, a best case result may be a momentary pause in production. On the other hand, a gas slug could represent the bitter end for a lift system that may have cost six figures. The problem has been serious enough to drive operators in the Permian Basin to adopt an artificial lift strategy not historically used there. Manufacturers have responded to the challenge by trying to make pumps more slug-tolerant, while others are marketing automatic shut-down systems for asset protection. HEAL Systems has taken a different tact with a technology that it says removes slugging from the production equation by separating the horizontal from the vertical well sections and regulating the flow between them. The emerging innovation is called a horizontal-enhanced artificial lift (HEAL) system. It has no moving parts, and can be connected to any variant of lift system. In May, the company gained commercial steam through a joint venture with Schlumberger. Partnering with the service company will give HEAL Systems (formerly known as Production Plus Energy Services) manufacturing support and elevate its visibility in key markets such as the Middle East. “We’ve recognized that as a small company, scaling up is a challenge,” said Jeff Saponja, the chief executive officer of HEAL Systems, adding that the goal of the joint venture “is to get this technology out there, grow the company faster, and have access to an incredible research capability to develop it to the fullest.” Production Plus shareholders retain the majority share of the joint venture, allowing it to continue to market HEAL units to other service outfits and pump manufacturers. Based on its more than 200 installations in the US and Canada, the company is touting data that show its product has extended the run life of pumps in shale wells by months and sometimes years. Highlighted case studies of multiple wells in different formations claim this longevity has generated production improvements of 40–100% above prior baselines.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.230
Teacher spread0.224 · 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.

Study designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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