Advancing Production Flow Profiling With Subatomic Fingerprints and Big Data Analytics
Bibliographic record
Abstract
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 196172, “Unleashing the Power of Smart Particles for Completion Diagnostics: Advancing the Production-Flow-Profiling Technology With Subatomic Fingerprints and Big-Data Analytics,” by Talgat Shokanov, SPE, and Pavel Khudorozhkov, QuantumPro, and Adilkhan K. Shokanov, Abai University, prepared for the 2019 SPE Annual Technical Conference and Exhibition, Calgary, 30 September-2 October. The paper has not been peer reviewed. This paper describes a smart-tracer-portfolio testing and design solution for multistage hydraulic fracturing which will, write the authors, enable operators to reduce operating cost significantly and optimize production in shale wells. The technology combines recently developed smart tracers with advanced subatomic measurements in an automated process with stringent quality control that assures precise tracer addition onsite and provides accurate and actionable completion diagnostics results at a fraction of the cost of production-logging testing, distributed temperature testing, or distributed acoustic sensing. Introduction Multistage hydraulic fracturing operations costs - including high-pressure pumping, proppant, and fluid - ranged from $2.9 million to $5.6 million per well in a typical US shale well in 2018, representing close to 60% of the total drilling and completion cost for each well. Yet industry studies reported that up to 50% of the clusters and stages and up to 40% of the fracture net-works do not produce in the current geometric factory-mode-completion approach, leading to estimates that up to 40% of the drilled and completed shale wells in North America alone could be uneconomical. Additionally, interactions between fractures in adjacent horizontal wells, and the costly negative effects of these interactions, have become the focus of much discussion and debate within the technical community. The impetus for this attention has been the effect of these interactions on productivity and the mechanical integrity of the parent wells. These issues drive the need for oil and gas operators to have more-accurate, affordable, and timely data on the performance of individual fracturing stages, measured intrawell communication, and temporary and long-term frac/frac connections to enable improved decision-making and optimization of multistage hydraulic fracturing operations as well as overall field development. The complete paper describes smart tracer technology, including a patented portfolio and fracturing-/completion-optimization work flow; laboratory testing and performance analysis; and integration with completion diagnostics. Smart Tracer Technology To control the effectiveness of multistage hydraulic fracturing stimulation treatments, it is essential to use special tracing methods based on the addition of the labeled substance to the proppant, water, or gas, and monitor the release of tracers with flowback water and produced oil and gas from the current well or nearby observation wells. Currently, conventional water- and oil-soluble chemical substances with fluorescent properties and ionic, organic materials, or radioactive isotopes, are used as the tracers. Tracers with fluorescent properties and ionic and organic materials are high-cost, limited to chemical-measurement techniques at a molecular level, and often have reported false-positive results for long-term communication. Environmental regulations in many countries prohibit the use of radioactive tracers (i.e., radioactive isotopes) because they pollute the environment and could contaminate subsurface layers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".