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Record W2892914459 · doi:10.2118/191741-ms

Experimental Proppant Conductivity Observations: Evaluating Impact of Proppant Size and Fluid Chemistry on Long-Term Production in Shales

2018· article· en· W2892914459 on OpenAlexaboutno aff
Abhinav Mittal, Chandra Rai, Carl Sondergeld

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHydraulic fracturingPermeability (electromagnetism)CompactionOil shaleEmbedmentMaterials scienceHydraulic conductivityGeologyGeotechnical engineeringPetroleum engineeringComposite materialSoil scienceChemistry

Abstract

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Abstract Propped hydraulic fractures have enabled economic hydrocarbon production from organic rich shales. Laboratory testing of proppants can help in systematic evaluation of different factors that can affect proppant performance. This study is focused on long-term conductivity measurements of proppant-packs at simulated reservoir pressure and temperature conditions. Mechanisms like proppant crushing, embedment, and diagenesis are investigated. Testing was done using a conductivity cell made of Hastelloy; allowing simultaneous measurement of fracture compaction and permeability. The proppant filled fracture (concentration: 0.75-3 lb/ft2) is subjected to axial load (5000 psi) to simulate closure stress. Brine is flowed through the pack at a constant rate (3 ml/min) at elevated temperature (250° F) over an extended duration of time (from 10-60 days). 20/40 and 60/100 mesh Ottawa sand were used in this study. The proppant-pack performance is evaluated between shale platens fabricated from Eagle Ford rock (58% clay by wt.; Nanoindentation Young's modulus - 16 GPa). Experiments on the 20/40 and 60/100 Ottawa sand (1.5 lb/ft2 proppant concentration) at elevated pressure (5000 psi) and temperature (250° F), spanning 10 days demonstrate that proppant size strongly impacts proppant performance. The proppant-pack permeability for 60/100 sand drops dramatically within a few hours. The 20/40 proppant permeability is double the permeability of 60/100 sand even after 10 days of testing. Approximately 60% compaction is observed over the test duration, with 28% contribution from proppant crushing and rearrangement, and 32% contribution from embedment. Particle size analysis of proppant grains and SEM images verify proppant crushing, fines migration and embedment as dominant damage mechanisms. Proppant embedment and crushing are observed to be dependent on the shales being tested. Fracturing jobs involve maintaining a basic pH environment for optimal performance of fluid additives for better proppant placement via control on viscosity. A second study was conducted to compare performance on similar Eagle Ford shale by altering the fluid chemistry (pH ~ 10.5) to understand the impact on permeability and compaction over time. Over a duration of 20 days, the permeability dropped from 120 darcy to 200 md. After 8 days, the pH:10 brine permeability was 10 times lower than pH:7 brine permeability. After 18 days, the fracture width reduced by 90%, indicating a creep behavior. High silica content (>20 ppm) was observed in the outlet brine. The proppant and rock surface were studied under SEM to investigate the role of secondary mineral growth during the drastic reduction of permeability. This study is focused on understanding fracture conductivity under as realistic near in-situ experimental conditions. Testing between shale platens at reservoir temperature and pressure conditions is more representative of subsurface environment. Dynamic measurements in the current study were conducted for long duration (spanning 10-30 days) using brine. This allows the study of effects of mechanical and chemical degradation of fracture conductivity, which has been used to separate the effects of crushing and embedment. Our results demonstrate that the fracture conductivity is dependent on proppant size and pH of the flowing solution.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.477

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 integrity0.0000.000
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 head0.051
GPT teacher head0.326
Teacher spread0.275 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2018
Admission routes1
Has abstractyes

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