Comparison of Streamline Reservoir Simulation of Brine Flow, Mixing and Barium Sulphate Induced Formation Damage with Observed Produced Water Chemical Data to Aid Scale Management
Bibliographic record
Abstract
Abstract In waterflooded reservoirs under active scale management produced water samples are routinely collected and analysed, yielding information on the evolving variations in chemical composition. These produced water chemical compositional data contain clues as to the fluid/fluid and fluid/rock interactions occurring in the subsurface, and are used to inform scale management programmes designed to minimise damage and enable improved recovery. In this interdisciplinary paper, the analyses of produced water compositional data from the Miller Field are presented and a 1D reactive transport model is developed to study possible geochemical reactions taking place within the reservoir through matching model results with observed produced water data. However, in the 1D reactive transport model, only one flow path was simulated; this does not fully represent the fluid flow and mixing behaviour in the reservoir. Therefore, this paper also presents a fully 3D reservoir simulation study for the Miller Field to evaluate brine flow and mixing processes occurring in the reservoir, using an available history matched streamline reservoir simulation model integrated with produced water chemical data. Conservative natural tracers were added into the modelled injection water, and then the displacement of injection water and the behaviours of the produced water in two given production wells were further studied. In addition, the connectivity between producers and injectors was investigated based on the comparison of production behaviour calculated by the reservoir model with produced water chemical data, and an assessment of the properties of the intervening faults was also performed. Finally, a model of BaSO4 scale precipitation was included in the model, and the simulation results with and without barite precipitation were compared with produced water chemical data (observed barium and sulphate concentrations in the produced brine). In general, the modelled and observed data were found to be in good agreement, but any discrepancies were in fact found to be very informative also. The model assumes scale deposition is possible everywhere in the formation, whereas in reality the near production well zones were generally protected by scale inhibitor squeeze treatments, and thus the discrepancies between modelled and observed data could be used to diagnose the effectiveness of the chemical treatments to prevent formation damage around the production wells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".