Automatic Optimization of Oilfield-Scale-Inhibitor Squeeze Treatments Delivered by Diving-Support Vessel
Bibliographic record
Abstract
Summary Oilfield-scale deposition is one of the important flow-assurance challenges facing the oil industry. There are a number of methods to mitigate oilfield scale, such as reducing sulfates in the injected brine, reducing water flow, removing damage by using dissolvers or physically by milling or reperforating, and inhibition, which is particularly recommended if a severe risk of sulfate-scale deposition is present. Inhibition consists of injecting a chemical that prevents the deposition of scale, either by stopping nucleation or by retarding crystal growth. The inhibiting chemicals are either injected in a dedicated continuous line or bullheaded as a batch treatment into the formation, commonly known as a scale-squeeze treatment. In general, scale-squeeze treatments consist of the following stages: preflush to condition the formation or act as a buffer to displace tubing fluids; the main treatment, where the main pill of chemical is injected; overflush to displace the chemical deep into the reservoir; a shut-in stage to allow further chemical retention; and placing the well back in production. The well will be protected as long as the concentration of the chemical in the produced brine is greater than a certain threshold, commonly known as minimum inhibitor concentration (MIC). This value is usually between 1 and 20 ppm. The most important factor in a squeeze-treatment design is the squeeze lifetime, which is determined by the volume of water or days of production where the chemical-return concentration is greater than the MIC. The main purpose of this paper is to describe the automatic optimization of squeeze-treatment designs using an optimization algorithm, in particular particle-swarm optimization (PSO). The algorithm provides a number of optimal designs, which result in squeeze lifetimes close to the target. To determine the most efficient design of the optimal designs identified by the algorithm, the following objectives were considered: operational-deployment costs, chemical cost, total-injected-water volume, and squeeze-treatment lifetime. Operational-deployment costs include the support vessel, pump, and tank hire. There might not be a single design optimizing all objectives, and thus the problem becomes a multiobjective optimization. Therefore, a number of Pareto optimal solutions exist. These designs are not dominated by any other design and cannot be bettered. Calculating the Pareto is essential to identify the most efficient design (i.e., the most cost-effective design.)
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".