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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".