Prediction of Scales in Boilers of Thermal Recovery Projects
Bibliographic record
Abstract
Abstract The production of silica in thermal petroleum recovery projects is a well known phenomenon, and considerable efforts at its control are a common feature of facilities engineering in such projects. In solutions of high pH, such as boiler feedwater and blowdown, however, the silicates of iron, calcium, magnesium and sodium are of greater interest than silica itself. Scaling by such silicates is usually predictable by means of computer programs that rely on free energy minimizations. Where instabilities for scaling are predicted in this way, often a large range of potential mineral deposits are identified as potential scale deposits. The reality is, however, that only one or two such minerals are ever found in the analysis of pigging solids. A simple method, that permits the prediction by non-chemists of both type and quantity of preferential scales, is derived and its use in SAGD water management and recycling schemes is illustrated. The effect of the presence of chelants in boiler feedwater may not prevent silicate scales, but merely shift the preferred scale. Sample recovery and handling, on the other hand, may cause a shift in scale preference under laboratory conditions, as opposed to facility conditions. Introduction The observation of scaling by metal silicates, rather than amorphous silica, is a phenomenon in SAGD operations that recognized relatively recently. A variety of alkaline earth silicates and mixed silicates have been observed1,2 in various unit operations of SAGD plants, including boiler tubes, production headers, and separation facilities. Analyses of fluid interactions in disposal wells also often predict formation damage due to precipitation of such silicates. The prediction or thermodynamic confirmation of the scales found analytically is usually accomplished by water chemistry software that is based on free energy minimization. The program used in this work is the well known SOLMINEQ program developed by the Alberta Research Council. Silicate Scales in SAGD A number of scales have been observed by various authors in the past few years. Table 1 provides a summary of silicate scale type and references. The most commonly encountered scales appear to be Talc, a magnesium silicate, and Tremolite, a more complex calcium magnesium silicate of the asbestos variety. From time to time, isomorphic substitutions are reported, among them Richterite, where one of the calcium atoms in Tremolite is replaced by sodium atoms. In general it is found that the predictive programs, when run with a given analysis, tend to predict a range of metal silicates. Given that waters in SAGD operations are generally quite soft, only one or two of the scales predicted will be found in practice, however. The purpose of this paper is to provide a simple method of predicting which of the predicted scales will be the preferred one. The importance of this work is the prediction of scale quantity. The predictive programs are able to predict a scaling index, which is not necessarily related to quantity. Demonstration Calculation Suppose we have an example fluid of the composition given in Table 2.
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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".