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Record W2913363763 · doi:10.2118/194330-ms

Determining the Most Effective Diversion Strategy Using Pressure Based Fracture Maps : A Meramec STACK Case Study

2019· article· en· W2913363763 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSPE Hydraulic Fracturing Technology Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
FundersChaparral Energy
KeywordsFracture (geology)Pressure dropDrillingDrop (telecommunication)Well stimulationStage (stratigraphy)GeologyPetroleum engineeringGeotechnical engineeringEnvironmental scienceMaterials scienceReservoir engineeringEngineeringPetroleumMechanics

Abstract

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Abstract This paper discusses a STACK (Sooner Trend Anadarko Basin Canadian and Kingfisher Counties) case study that determined the effectiveness of different diversion techniques, including pods, sand ramps with sand slugs, rate cycling, and utilization of the completions order to control fracture growth. A secondary goal of this study was to evaluate the suitability of pressure-based fracture maps and oil and water phase tracers in monitoring diverter effectiveness. Effectiveness of a given diverter technique and diverter drop was evaluated using the two techniques on a 3-well pad. The three wells were completed using a combination of: 4 pods per treatment interval6 pods per treatment interval8 pods per treatment intervalhigh-volume proppant loading per treatment interval The effectiveness of the diverter drop was evaluated using each of the diagnostic techniques listed above. The pressure-based fracture analysis uses the pressure response recorded in an isolated stage in the monitor well to compute fracture geometry and the rate of growth of the fracture dimensions. The effectiveness of a given diverter drop is classified into one of four possible categories: stop dominant fracture growth, impede dominant fracture growth, no impact on growth of dominant fracture and accelerate the growth of dominant farcture. These results were then compared with the analysis from oil and water phase tracers and treatment pressure analysis. Successful (effective) diversion was observed on 82 % of the stages with pods compared to 64% successful diversion where sand ramps were used. In addition, stages using 8 pods for diversion had a 15% reduction in average fracture half-length compared to stages using 4 pods. Fracture height was better controlled through the order of completions of the stages between 3 wells. Completing the middle well in the upper part of the zone ahead of the two outer wells in the lower part of the zone, controlled the vertical height growth of the two outer wells. The offset pressure-based analysis proved to be as effective in accurately diagnosing the diverter effectiveness and provided a significant cost and timing advantage compared to other diagnostic techniques.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.218
Teacher spread0.208 · 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