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Record W2299937923 · doi:10.2118/179161-ms

Recent Advancements in Far-Field Proppant Detection

2016· article· en· W2299937923 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSPE Hydraulic Fracturing Technology Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
FundersSandia National LaboratoriesConocoPhillips
KeywordsPetroleum engineeringCompletion (oil and gas wells)GeologyFracture (geology)Hydraulic fracturingDrillingLead (geology)Point (geometry)Computer scienceMining engineeringEngineeringGeotechnical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Abstract The combination of multistage hydraulic fracture treatments with horizontal drilling technology has been the primary driver to the successful development of resource plays. More than 85% of wells drilled in North America today employ these methods. However, while these technologies have been wildly successful, only recently has the industry begun to address in earnest, the efficiency of current practices. These completion and development optimization efforts require an understanding of which portions of the reservoir have not been adequately contacted/stimulated and are thereby failing to contribute to production, and ultimate hydrocarbon recovery. Understanding where the proppant is located, both near- and far-field, is the starting point for these evaluations, and is the basis for this paper. Traditional fracture mapping technologies provide indirect estimates of fluid distribution within the fracture network. However, there is little direct correlation between fluid distribution and proppant location, and since most unpropped portions of fractures rapidly collapse, identification of the proppant location better represents the region which contributes to ultimate recovery. Near-wellbore detection of proppant can provide insight into whether all perf clusters (in the case of plug and perf) have received proppant as well as the impacts of proppant overflush. Conversely, accurate determination of far-field proppant placement will affect everything from well and stage spacing, to stage design and refrac candidate selection, and allow significant optimization of diversion techniques. While knowledge of both near- and far-field proppant location is necessary for the industry to overcome the single-digit recovery factors that are now projected in many unconventional plays, far-field proppant detection techniques have been largely absent to date. This paper briefly reviews the current "state of the industry" regarding near-wellbore proppant detection technology. It then presents a novel far-field proppant detection technique which utilizes electro-magnetic differencing and a specialty detectable proppant. This includes a description of the technology as well as the methodology of the technique. In addition, the paper reviews the design and results from a recent (first-ever) field deployment of this technology in a horizontal Permian Basin well. Visualization of the proppant in the far-field is also shown. This paper should be beneficial to all engineers and technologists currently interested in evaluating completion efficiencies as well as fracture stimulation effectiveness. Understanding proppant location in both the near- and far-field regions has significant impact on well spacing, stage and perf cluster spacing, and ultimate recovery from stimulated horizontal wells.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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