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Record W2946708876 · doi:10.2118/0619-0072-jpt

Phosphonate-Based Inhibitor Reduces Scaling Potential of Seawater

2019· article· en· W2946708876 on OpenAlexaboutno aff
Chris Carpenter

Bibliographic record

VenueJournal of Petroleum Technology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsSeawaterPhosphonateSalinityEnvironmental scienceChemistryEnvironmental chemistryEnvironmental engineeringChemical engineeringGeologyOceanographyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of “Mitigation of Scaling Potential of Seawater in High-Temperature Environment Using Phosphonate Scale Inhibitor,” by Raafat M. Yamak and Hisham Nasr-El-Din, SPE, Texas A&M University; Sabiq Rahim, SPE, Halliburton; and Moussa Taleb, University of Calgary, prepared for the 2019 SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, 16–18 April. The paper has not been peer reviewed. In this study, a laboratory analysis was conducted to study the effect of a phosphonate-based scale inhibitor on a mixture of hypersaline Arabian Gulf seawater and formation water under high-temperature/high-pressure conditions. The objective was to identify the minimum scale-inhibitor concentration required at various temperatures to achieve a cost-effective solution in minimizing the formation of common oilfield scales. This research pushes the thermal constraints of a phosphonate-based scale inhibitor to 330°F to test its efficiency and treatment integrity. Introduction Produced water, seawater, and nanofiltered seawater have been explored as environmentally friendly and cost-effective alternatives to fresh water in fracturing fluids at different ratios. Consequently, total-dissolved-solids (TDS) levels, salinity, and bottomhole temperatures have increased, making scale inhibitors more important than ever. In this study, raw Arabian Gulf seawater and a water mixture from the Jafurah formation was used at various ratios and at different temperatures to determine the efficiency of a phosphonate-based scale inhibitor in the presence of ion complexes. High scale formation was associated with the ionic effect on the fluid, especially because of the high content of sulfate in seawater and high barium and calcium concentrations in connate water. Scale-advisory-software results indicated that barium sulfate was the major scale. Additionally, specific ions can affect the pH of the fluid severely, thereby inhibiting the operational function of the buffer systems. Scaling is a natural byproduct of seawater-based fracturing. As a result, various water treatments have been implemented to decrease scale formation. One such method involves nanofiltration. Experimental results have shown that nanofiltration caused sulfate reduction in seawater sources down to 300 ppm. This lowers the scaling tendency to a point at which it is controllable by conventional chemical treatments. To address the issue of freshwater scarcity and associated treatment costs of alternatives such as waste water, the use of raw seawater has received attention. The TDS content of the source water used in this paper is one of the highest in the world, given that the Arabian Gulf is known for its hyper-saline conditions. Furthermore, the cations present in the water, namely calcium and magnesium, are known to cause problems in the formulation process of hydraulic-fracturing fluid. As a result, it is expected that certain fracturing-fluid additives must be increased to meet these challenges and ultimately create a stable seawater-based fracturing-fluid system with appropriate gelation timing that meets industry standards. This study aims to find an alternative source to freshwater-based fracturing fluid and contribute to the study of scale inhibition as dirtier water sources are considered.

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 score0.003
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.211
Teacher spread0.207 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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