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Enregistrement W4386594486 · doi:10.2118/215652-ms

Field Deployment of a Novel Shaped Charge with Intelligent Tracers Integrated in its Degradable High Entropy Alloy (HEA) Liner for a Clean, Low-Skin, Perforation Tunnel

2023· article· en· W4386594486 sur OpenAlexaboutno aff
Ting Roy, Kamel Ben Naceur, Casey Harrison, J. Reid Shelton, Aaron Christopher. Hall, Ian Henkes, Kenny Jones, Christian Wilkinson, Hifzi Ardic, Indranil Roy

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueForce Microscopy Techniques and Applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPerforationGeologyPetroleum engineeringMaterials scienceGeotechnical engineeringComposite material

Résumé

récupéré en direct d'OpenAlex

Abstract A debris free, clean perforating tunnel with lower skin offers better reservoir connectivity, better production and slower decline. An equal entry diameter perf-hole (EHD) creates a uniform "sprinkler" system offering less resistance during stimulation and high cluster efficiency. A larger EHD has several benefits, primarily it will lower "Perf Friction" (Pf), allowing lower treatment pressures, greater flow across each perforation, less chances of a screen out, uniform drainage, and engineered completions where larger EHD perforations can be placed at the "Toe" with decreasing EHD perforations towards the "Heel" to offset the effects of wellbore fluid friction. The first field introduction of CLEAR charges was conducted in 5 different Permian wells in diverse shale plays and burial depths, with frac-gradients from 0.65 psi/ft to 0.90 psi/ft. To assess its performance and establish its superiority, commercial equal hole charges of equivalent 0.4-inch EHD were shot in parallel to CLEAR charges every other stage in these wells. In addition, to establish the effect of a larger EHD on stimulation, 0.5-inch EHD tracer charges were shot in 10 randomized stages in each of these wells. Field test results helped established the effects of clean perforation tunnels, with reduced perf friction even in consolidated formations with high frac gradients, a lowering of time to design rate, pad volumes, also bettering other key performance metrics. Recently there has been a lot of advancement in the area of "Nanoparticle Tracers" which are being evaluated as means for remote sensing. In frac operations such tracers can be functionalized for deployment in reservoirs to remotely monitor stage performance from zonal returns of the nanoparticles to surface with flowback. Combining these concepts has led to a game-changing application, an industry first tracer shaped charge with high entropy degradable alloy (HEA) liner for a slug and debris free, clean perforating tunnel with low skin. A novel study was undertaken to deploy nanoparticle tracers in reservoirs via shaped charges. Classified and functionalized rare earth oxide nanoparticles manifesting unique emission and absorption spectra and engineered decay times based on their optical, physical, luminescent properties, are identifiable in parts per billion (ppb) dilution. These were designed to survive detonation events and integrated in the HEA charge liners. Flowback emulsions sampled from client wells were scanned with collimated light of tailored wavelength to acquire unique after-glow tracer spectra. The presence of the meta-material tracers in the flowback samples confirmed successful return of the nano-particulates to the surface. Expanding the scope of application(s) of intelligent tracers to related domains, we envision getting a better understanding of the dissolution and precipitation of minerals and its impact on the transport of fluids in porous media as essential to various subsurface applications, including shale gas production using hydraulic fracturing ("fracking"), CO2 sequestration, or geothermal energy extraction. In our paper we discuss patent pending concepts to experimentally study this phenomenon at lab scale (Phase – I of the study) and develop an effective high temperature scale inhibitor with integrated (functionalized) REO tracers to map fractured rock and determine initial permeability of the rock. Flushing and inducing scale formation in the pore structure and fracture network, studying the loss in permeability over time with varying environmental parameters (pressure and temperature) will allow calibrating and tuning the HT scale inhibitor, making it an effective treatment of injection fluids / formation flush for EGS. Use of these novel large EHD tracer charges for "Carbonate Acidizing" in the Middle East and North Africa (MENA), where approaches are highly dependent on the rate of mass transfer should be a game changer. During matrix acidization, low injection rates lead to a Damköhler number > 1, where rapid acid reaction results in face dissolution and an ineffective treatment (Hoefner, M.L. and Fogler, H.S. 1988). Larger EHD perforations relieve this bottleneck (Fredd C.N. 2000). Our novel HEA degradable liners are expected to bridge these unmet technology gaps. Last but not least, scaling up for commercialization of our large EHD tracer charges, encompasses transport of classified and blended raw powdered raw materials used in the CLEAR High Entropy Alloy (HEA), several of which are considered Dangerous Goods (DG). Shipping by land, sea or air, as such is governed by various country specific and international codes or regulations. These include the Code of Federal Regulation Title 49 in the US (49CFR 100-180), the Transport of Dangerous Goods Regulations (TDGR) in Canada, Technical Instructions for the Safe Transport of Dangerous Goods by Air (ICAO TI), International Air transport Association Dangerous Good regulations (IATA DGR), International Maritime Dangerous Goods Code (IMDG), and International Carriage of Dangerous Goods by Road (ADR). Alongside obtaining Federal Explosives License (FEL), compliance to The Hazard Communication Standard (HCS) (29 CFR 1910.1200(g)) by providing safety data sheets (SDS) to specific markets, meeting local requirements have been adhered. This has been highlighted in our article.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,277
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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