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Record W2325159361 · doi:10.2118/175154-ms

Advances in Flowback Chemical Analysis of Gas Shales

2015· article· en· W2325159361 on OpenAlexaff
Ashkan Zolfaghari, Yingzhe Tang, Jordan Holyk, Mojtaba Binazadeh, Hassan Dehghanpour, Doug Bearinger

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsNexen (Canada)University of Alberta
Fundersnot available
KeywordsChemistryDivalentExtraction (chemistry)Aqueous solutionOil shaleNitric acidInorganic chemistryEnvironmental chemistryChromatographyGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Recently, flowback chemical analysis has been considered as a complementary approach for evaluating fracturing operations and characterizing reservoir properties. Understanding the source of flowback salts and the mechanisms controlling the water chemistry is essential but also challenging due to the complexity of shale-water interactions. In this study, samples of flowback water and downhole shales are analyzed to investigate the mechanisms controlling the chemistry of flowback water. The water samples at different flowback times and the shale samples are collected from three wells completed in the Muskwa, Otter-Park, and Evie members of the Horn River Basin. The water samples consist of aqueous solution and precipitated salts. The water samples are digested in nitric acid to dissolve the precipitated salts, and are analyzed at both intact and acid-digested conditions using ICP-MS. The flowback salts are weighted and analyzed using XRD and SEM-EDXS. A sequential ion-extraction is performed on the shale samples; and the extracted ions are categorized into three tiers of loosely-, moderately-, and strongly-attached ions. The concentration of monovalent cations in both intact and acid-digested samples is higher than that of divalent cations. Also, the concentration of all cations is higher in the acid-digested samples compared with that in the intact samples. The ratio of divalent cations concentration in the acid-digested samples to that in the intact samples is higher than that for the monovalent cations. This ratio increases for the divalent cations over time, while it remains constant for the monovalent cations. Additionally, for the acid-digested samples the monovalent cations concentration has an initial sharp increase followed by a slower increase at later flowback stages; while the divalent cations concentration increases continuously over time. These results suggest that the majority of the ions in the early flowback water are loosely-attached monovalent ions. These ions can be originated from the mixing with in-situ formation brine, dissolution of soluble precipitated salts, or leaching of exchangeable cations from the clay minerals. Similarly, the role of relatively slow water-rock interactions (such as leaching of divalent exchangeable cations, e.g. Ca2+,) increases at the later flowback stages. XRD and SEM-EDXS analyzes of the flowback salts indicate that sodium chloride, potassium chloride, and calcium carbonate are the major salts. The sequential ion-extraction reveals that the majority of the monovalent cations are in the loosely-attached tier. However, majority of the divalent cations are moderately- /strongly-attached to the rock. The strongly-attached portion of the ions is determined by acid digestion of the rock sample at the final stage of sequential extraction process. These strongly-attached ions cannot be easily released by hydraulic fracturing and therefore, has small effect on the flowback water chemistry.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.248
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2015
Admission routes1
Has abstractyes

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