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Record W2086123362 · doi:10.2118/0107-0028-jpt

Technology Update: Practical Application of Intelligent Technology in Low-Production Environments

2007· article· en· W2086123362 on OpenAlexaboutno aff
JPT staff

Bibliographic record

VenueJournal of Petroleum Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware deploymentChokeProduction (economics)EngineeringModular designIsolation (microbiology)Control valvesEnhanced oil recoveryInvestment (military)Petroleum engineeringOil fieldWell controlComputer scienceMechanical engineeringDrillingOperating systemElectrical engineering

Abstract

fetched live from OpenAlex

Until recently, implementing intelligent-completion (IC) technology to optimize hydrocarbon production in land-based or shallow water, low-production environments has been economically prohibitive because of the cost of the required equipment compared to its return on investment. Alternatives now exist for reduced capital investment and operating costs, enabling operators to re-evaluate the economic feasibility of IC deployment in mature fields, large fields requiring pressure maintenance, secondary-/tertiary-recovery schemes, and multilayered fields. Approaches for moderate-return environments include an introductory-level option for ICs launched by WellDynamics that incorporates streamlined designs and the use of standard metallurgy. Proprietary SmartWell MC completion technology focuses on the ability to improve management of the recovery methods associated with enhanced oil recovery (EOR), providing real-time monitoring as well as zonal isolation and subsurface flow control in both vertical and horizontal well configurations. The use of this technology in EOR projects offers several benefits that promote enhanced recovery and improved economics, including enhanced recovery of individual layers through commingled production, improved pressure maintenance, controlled drawdown and fluid production from individual zones, and superior management of water cut. Open/close and choking versions of hydraulically actuated interval-control valves (ICVs) in a range of sizes, production and zonal-isolation packers, and modular gauge packages make up the MC line of technologies. The MC2 open/close ICV enables isolation of individual reservoir zones while the MCC ICV enables operators to choke or regulate injection/production by adjusting the valve through multiple incremental positions. Both MC ICVs accommodate a flow rate that exceeds 3,000 BOPD or 5 MMscf/D of gas and are equipped with corrosion-resistant coatings for downhole endurance. The valves can be used with either an automated or a manual control system to meet specific operational requirements. The MC production packer is a single-string, retrievable production packer rated at 5,000 psi for cased hole. The slipless MC isolation packer allows perforated intervals to be segregated in cased hole below a production packer, where tubing loads and pressure differentials are moderate. Eight control lines can be fed through each of the packers. Pressure and temperature-sensing capabilities supplement the MC series. These sensor packages are modular in construction, allowing for adaptation to a multitude of tubing sizes, without altering the primary packaging configuration. Application in Swan Hills EOR Project SmartWell MC products were installed by Devon Energy in a miscible-solvent injection well within the Swan Hills Unit No. 1 oil field, located in north-central Alberta, approximately 120 miles from Edmonton. Devon has been the operator through almost all of the field's life and has taken it through primary, secondary, and tertiary recovery. The chief motivating factors for installing the equipment in this development were the opportunities to reduce well intervention and to accelerate the injection schedule associated with a single well. Considerable workover activity to manage the injection profile had already occurred in the trial well, which was judged to be a particularly risky workover candidate because of issues associated with corrosion, injection debris, and risks to cement integrity caused by frequent acid stimulation.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.283
Teacher spread0.274 · 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 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
Published2007
Admission routes1
Has abstractyes

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