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Record W4308453702 · doi:10.18280/ijdne.170506

Chemical Analysis of Groundwater and Wastewater in the Area of the Tengiz Deposit of the Atyrau Region of the Republic of Kazakhstan

2022· article· en· W4308453702 on OpenAlexvenueno aff
Tauova Nursaule, Mansiya Yessenamanova, Kozhakhmet Kossarbay, Zhanar Yessenamanova, Anar Tlepbergenova, Samal Shamshedenova, Gaukhar Batyrbayeva, Sanshe Maden

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterMagnesiumWastewaterSulfatePotassiumChlorideChemistrySodiumBicarbonateEnvironmental chemistrySalt (chemistry)Inorganic chemistryEnvironmental engineeringEnvironmental scienceGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

This article analyzes the content of the chemical composition of groundwater and wastewater of the Tengiz deposit located in the Zhyloy district of Atyrau region of the Republic of Kazakhstan. This analysis is necessary to determine the deposition of inorganic salts in oilfield equipment. The analysis shows that the content of chloride anions and magnesium cations prevails in groundwater and wastewater. Thus, the content of chloride anions exceeds the content of other anions up to 2-3 times of sulfate anions in groundwater and wastewater and up to 2.5-5 times of bicarbonate anions in groundwater and 17-27 times in wastewater. Among the cations, the maximum values are characteristic of magnesium cations, whose content exceeds the calcium content by more than 3.5 times in wastewater and more than 6.5 times in groundwater. In addition, the magnesium content exceeds more than 5 times the content of the sum of sodium and potassium ions. Thus, according to the results of the study, it was determined that the main salts affecting the oilfield equipment at the Tengiz field are magnesium chloride salts.

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)0.001
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 score0.180
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.008
GPT teacher head0.207
Teacher spread0.200 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Design & Nature and EcodynamicsSame topicOil and Gas Production TechniquesFrench-language works237,207