New Correlation for Iron Sulfide Stability in Crude Oil Desalting Plants Wastewaters
Bibliographic record
Abstract
Abstract A common problem, which is critical to deep disposal of wastewaters, is deposition of scale in the pipelines, in the tubing, in the disposal well's perforations and in the receiving formation. Such deposition is attributed to incompatibility of the wastewaters being mixed or of mixture's interaction with the formation's rock and connate water. A generally good practice prior to injection is to do laboratory experiments to test the compatibility of these wastewaters. However, experience shows that compatibility tests do not reliably give an accurate indication of precipitation. Here, we show how to develop relevant correlations, which are useful for predicting precipitation of ions from such mixtures and then provide a practical example, which is for disposing different wastewaters collected from southwest Iranian desalting plants. Introduction In some Iranian oilfields, the production of salty wet crude is an important concern; many wells were shut in for lack of treating facilities. The water, which produced with this crude, is a water containing salts in a concentration of 150,000 to 220,000 ppm. Moreover, this crude contains particles of salt and stable salty water-in-oil emulsions. Treating this crude to remove salt and salty water is not an easy task [1]. Each treater has the following train of steps;an API separator separates free water from the crude,wash water and surfactant are mixed with the crude to form an emulsion,an emulsion-breaking surfactant is mixed with the emulsion, andan API separator separates free water from the crude. In practice for each treating plant, two or three treaters are placed in series to reduce the salt to an acceptable limit. However, for many wells' crude, such treating is inadequate. After much study, an electrostatic precipitator was added to one plant's treaters to remove droplets of water from the crude. This addition sufficed. Consequently, electrostatic precipitators were added progressively to Iran's treating plants and new treating plants were built. By the end of 2004 electrostatic precipitators had been installed in more than 20 treating plants; their total capacity is 207 Mm3/Day (1.3 MMSTB/Day) of treated crude. Yet new treating plants are being added to meet expected total production of wet crude, 400 Mm3/Day (2.5 MMSTB/Day) by year 2007. Figure 1 depicts the schematic of a typical treating plant in Iranian oil fields. The wastewater from a treating plant has volume, which typically is about 15% of the crude being treated and contains about two-thirds formation water and onethird wash water. The wastewater treated before it is injected into disposal wells; the wastewater treating system consists of a skimmer tank, API gravity separator, filter, and disposal tank. However, the water from one formation differs from that from another. Experience shows that when one treated wastewater is mixed with another, then deposition of scale in the pipelines, in the tubing, in the disposal well's perforations and in the receiving formation may occur. When this occurs, such waters are said to be incompatible with one another.
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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) | 0.001 | 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 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".