Understanding Trends in Sulphate Concentrations in Produced Water Within Oilfields Under Seawater Flood and With Calcium-Rich Formation Water
Bibliographic record
Abstract
Abstract Sulphate concentration of produced water is a controlling factor in the scaling tendency of sulphate minerals (BaSO4, SrSO4 and CaSO4). In reservoirs under seawater flood, where the formation water is calcium-rich (>5,000 mg/l), and the reservoir temperature is above moderate levels (>100°C), produced water sulphate concentrations, sulphate mineral scaling potentials and therefore scale mitigation costs are often lower than expected due to deposition of sulphate scaling minerals in the reservoir. To obtain more realistic predictions of sulphate mineral scaling potentials and scale mitigation costs there is significant interest in trying to understand the factors controlling produced water sulphate concentrations and to simulate these data. Various models have been used to simulate produced water sulphate analyses but only reactive transport reservoir simulators incorporate the capability to model the most important factors determining produced water sulphate concentrations: reservoir reactions and mixing in and around the wellbore. However, even in this case the underlying reservoir models are often uncertain and the approach costly and time-consuming. In this study we present a new, two-water mixing model which assumes that water entering a production well is simply a mixture of (a) formation water and (b) an equilibrated mixture of formation water and seawater from which sulphates have precipitated in the reservoir (mixing zone water). This model can be used to explain trends in produced water scaling ions where lower than expected sulphate mineral scaling potentials are observed. By matching trends in produced water scaling ions, the model can be used to determine the variation in production proportions of the two waters, their compositions and seawater contents over time. When applied to wells of the Clyde Field, trends in sulphate and barium produced water analyses are found to reflect a reduction in the proportion of formation water and an increase in that of mixing zone water (and its seawater content) over time. For Gyda wells, the same results were obtained except that later in production, production of formation water ceases and two different mixing zone waters are produced. The model results are what would be expected for wells being progressively affected by a seawater flood and they have also been used to provide reasonable predictions of concentrations of other scaling ions in the produced water. Therefore, although the model is a significant simplification of mixing conditions in and around the well, it does appear to provide reasonable results that are easily obtained. The model results have a number of possible uses including (a) explaining trends in produced water scaling ions and lower than expected sulphate mineral scaling potentials, (b) providing alternative data for undertaking well scaling potential calculations and determination of laboratory MICs, (c) helping identify inadequately preserved samples and (d) potentially constraining the reservoir model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".