The Application of Streamline Reservoir Simulation Calculations to the Management of Oilfield Scale
Bibliographic record
Abstract
Abstract Inorganic scales precipitate in oilfield systems - downhole in the reservoir, in the production flow tubing, and in surface facilities - as a consequence of thermodynamic changes that affect the flowing brines. These changes may be induced by temperature or pressure changes, or by mixing of incompatible brines. While much work has been performed to study the effect of thermodynamic changes such as pressure decrease or temperature increase on scale precipitation, it is only recently that a body of work has been developed on the impact that the dynamics of brine mixing in the reservoir has on scale precipitation in situ. Much of this work has been conducted using finite difference simulators, which are handicapped with regard to these calculations in that numerical dispersion effects can be orders of magnitude greater than physical dispersion. The introduction of chemical reaction calculations into streamline simulation models presents a very significant opportunity for improving the accuracy of such calculations. While numerical dispersion effects for immiscible calculations (eg water displacing oil) can be countered by pseudoisation of the relative permeability functions, in finite difference models it is difficult to control numerical dispersion for miscible displacements - eg seawater (with a SO4 concentration) displacing formation water (with a Ba concentration), which may lead to scaling in the reservoir (BaSO4 precipitation). Streamline simulation reduces the numerical errors for both miscible and immiscible displacement - so making the scaling calculations much more accurate. The objective of this paper is to study the application of a streamline simulator that has the appropriate chemistry modelling capabilities to simulate realistic reservoir scenarios. The workflow will consist of two stages: Study of synthetic systems to identify the impact of brine mixing in simple scenarios (eg single layer and multi layer quarter five spot patterns) Application of the technique to full field reservoir systems where produced brine chemistry data are available. The calculations performed will demonstrate where, and under what conditions, scale precipitation takes place in situ in the reservoir, and what the resulting impact on the chemical composition of the produced brine will be. This information is key in the planning of management of oilfield scale, especially in deepwater developments where options for scale mitigation may be limited. As a result of this work, it is expected that guidelines will be drawn up for the use of streamline simulation, particularly during the FEED stage of new projects, and when considering infill well drilling in mature projects. These guidelines will be illustrated by reference to field examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".