Scale-up Methods for Micellar Flooding and their Verification
Bibliographic record
Abstract
Abstract Design of micellar floods is largely based on laboratory experiments, which are usually unscaled. This paper describes scaling criteria for the process, derived from the basic flow equations, using Dimensional Analysis and Inspectional Analysis. The derivations are based on three-phase (oleic, emulsion, and aqueous), six-component (oil, water, surfactant, polymer, monovalent ion, and divalent ion) flow in a porous medium. The general scaling criteria were simplified for core floods, and verified by micellar floods in scaled models. Model and prototype were geometrically scaled Berea cores. Prototype performance was predicted using the model results and compared with the actual prototype results. Good agreement was obtained in most cases between the actual and predicted oil production histories, showing the validity of the scale-up. The scaling criteria derived can be used for designing a micellar flood. Implications of partial scaling are discussed for field applications. Introduction Micellar flooding process is one of the proven chemical recovery methods for the tertiary recovery of light oils. The process consists of injecting a micellar solution slug (5 - 10﹪ rock pore volume) and a polymer buffer (40 - 50﹪ pore volume), followed by continuous injection of water (drive water). Micellar solutions are surfactant stabilized oil-water micro-emulsions. Often, they also contain co-surfactants, such as alcohols, for viscosity control, and salts to improve solution properties. Micellar solutions are effective in increasing the Capillary Number, which is crucial for the mobilization and recovery of tertiary Oil(1). Polymer buffer, usually a dilute polymer solution (about 500 ppm), provides mobility control behind the displacement front so that most of the residual oil is mobilized and banked before the drive water dissipates the micellar slug. The process has been evaluated in thirty field tests(2) and was found to be technically successful, having a process efficiency (oil recovered-to-slug volume ratio) of three to four. Recently, Thomas et al.(3) showed that process efficiency can be improved to 12 - 15 through the use of multiple slugs and graded slugs instead of a single micellar slug. Economics of the process remain unattractive, mainly due to the cost of chemicals and the initial capital outlay in the development of the process for a particular field, as well as low oil prices (< $20/bbl). Chemicals that are better adapted to reservoir conditions, and laboratory studies representative of field conditions will improve the economic feasibility of the process. Laboratory results based on scaled model experiments will reduce the risk in extending them to field. Scaling criteria derived for the process were discussed in a previous paper(4). Mathematical Model The micellar flooding process can be described mathematically for simplified situations, e.g., considering the oil (o) to be one component, surfactant (s) another, and water (w), polymer (p), monovalent ions (m), and divalent ions (d) similarly single components. The concentration of a particular component in a given phase is expressed as a mass fraction Cphase, component. Diffusion and dispersion is assumed to occur in the case of surfactant (s), polymer (p), monovalent ions (m), and divalent ions (d). It is assumed that the coordinate axes are oriented in the direction of flow.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".