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Record W3156132752 · doi:10.2118/185772-pa

Impact of Temperature on Scale Formation in Chalk Reservoirs

2018· article· en· W3156132752 on OpenAlexafffund
Oleg Ishkov, Roberta Guarnieri, M. M. Jordan, Eric Mackay

Bibliographic record

VenueSPE Production & Operations · 2018
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsNalco (Canada)
FundersU.S. Geological SurveyEnergi SimulationHeriot-Watt University
KeywordsBrineSeawaterCarbonateStrontiumGeologySulfateMineralogyGeochemistryChemistryOceanography

Abstract

fetched live from OpenAlex

Summary Operators are collecting abundant produced-water data that are often underused. Produced-water-composition data provide clues related to the geochemical reactions that are occurring in the subsurface. This information can be useful for monitoring interwell connectivity and predicting and managing oilfield scale resulting from brine supersaturation. Coupling thermodynamic calculations with produced-water analysis helps to identify geochemical effects that could affect oil recovery. This work addresses the difference that reservoir temperature has on geochemical reactions in carbonate reservoirs by comparing data from two offshore fields and identifying the rock/brine and brine/brine reactions that will affect scale management. Two seawater-flooded chalk fields located near each other were selected as candidates for comparison. The temperature of one field is 130°C, whereas for the other field, it is 90°C. Produced-water samples (a total of 6,800) from these two fields were analyzed, and the compositional trends were plotted to identify the deviation from conservative (nonreacting) behavior. The compositional trends were then grouped to identify if there were common features between wells. This analysis was complemented by 1D reactive-transport modeling to identify the reactions that would be consistent with the observed trends. Two groups of wells were identified within each reservoir on the basis of the produced-brine compositional behavior. Each well group exhibits a distinct ion-trend behavior, especially with respect to barium, calcium, strontium, and magnesium concentrations—because these are divalent cations that are abundant in the formation brines. The breakthrough of sulfate, a component primarily introduced during seawater flooding, varies very significantly between the two groups in each case. In one grouping, the sulfate is barely retarded, and it breaks through at seawater fractions lower than 10%. In the other grouping, however, sulfate does not break through until the seawater fraction in the produced brine exceeds 75%. This retardation of sulfate occurs most strongly in the hotter reservoir, and this might be attributed to the lower solubility of the calcium sulfate mineral anhydrite at a higher temperature. The retardation of sulfate then means that barium is produced at higher concentrations because barite precipitation in the reservoir is thus restricted, caused by sulfate being the limiting ion. However, some sulfate stripping does occur in the cooler reservoir, despite the higher solubility of anhydrite. Furthermore, in all cases, magnesium is retarded, with some groupings exhibiting the complete stripping of magnesium from the injected seawater. The magnesium-stripping behavior is reproduced in the reactive-transport models when calcium- and magnesium-replacement reactions are allowed. This phenomenon has been observed elsewhere in coreflood experiments, and it also contributes to the sulfate stripping through the promotion of anhydrite precipitation within the rock. This process, which is beneficial in terms of reducing the scale risk, is more pronounced at higher temperatures. Therefore, higher-temperature chalk reservoirs might act as natural sulfate-reduction plants, reducing scaling, souring risks and, thus, operating costs of the fields.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations4
Published2018
Admission routes2
Has abstractyes

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