Total Phosphorus Recovery in Flowback Fluids After Gelled Hydrocarbon Fracturing Fluid Treatments
Bibliographic record
Abstract
Abstract A previous work(1) described refinery plugging caused by volatile phosphorus components originating from phosphate ester oil gellants. Also documented were two successful field trials of new phosphonate ester oil gellants shown to address this problem. Another paper(2) presented the results of additional field testing of phosphonate ester gellants directed at the optimization of cost and performance. A maximum of 0.5 ppm volatile phosphorus in crude specification has been proposed to address costly unplanned refinery shutdowns. This specification is based on what is considered achievable through a combination of new chemistry and typical field dilution. However, this specification is based on average concentrations of phosphorus added to the oil to gel it and assumes the oil is phosphorus free to begin with. In some flowback studies, total and resulting volatile phosphorus concentrations greatly in excess of that added have been observed. In addition, refinery plugging is more the result of total phosphorus throughput than peak concentrations at any one point. Therefore, an understanding of total phosphorus recovery in addition to peak concentrations is needed. The objectives of this paper are to study:Total percent recovery of phosphorus originally added as phosphorus based gellant;Total percent recovery of volatile phosphorus as a function of total phosphorus;Peak concentrations of total and volatile phosphorus;Phosphorus concentrations in new and reused fracturing fluids before the addition of gellants; and,Potential explanations for phosphorus concentrations significantly higher than those originally added, which include phosphorus removal resulting in a positive/negative initial mass balance and long-term phosphorus removal with respect to overall mass balance. Introduction Unconventional gas reservoirs, including tight gas, shale gas and coalbed methane, are becoming critically important components of current and future gas supply. These reservoirs often present unique stimulation challenges. The use of water-based fracturing fluids in low-permeability reservoirs may result in the loss of effective fracture half-length caused by phase trapping associated with the re-tention of the introduced water-based fluid into the formation. This problem is increased by the water-wet nature of most tight gas reservoirs (where no initial liquid hydrocarbon saturation is, or ever has been, present) because of the strong spreading coefficient of water in such a situation. The retention of this increased water saturation in the pore system can restrict the flow of produced gaseous hydrocarbons such as methane. Capillary pressures of 10 ? 20 MPa or higher per thousand can be present in low-permeability formations at low water-saturation levels. The inability to generate a sufficient capillary drawdown force using the natural reservoir drawdown pressure can result in extended fluid-recovery times or the permanent loss of effective fracture half-length. Furthermore, use of water in subnormally saturated reservoirs may also reduce permeability and associated gas flow through a permanent increase in water saturation of the reservoir. Secondary costs such as rig time for swabbing can add to the negative economic impact. The effects of fracturing fluid retention on gas flow in the fracture face can be as important a consideration as fracture conductivity when designing a treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".