Experimental Design and Evaluation of Surfactant Polymer for a Heavy Oil Field in South of Sultanate of Oman
Bibliographic record
Abstract
Abstract Heavy oil reservoirs remain challenging for surfactant-based EOR. In particular, selecting fine-tuned and cost effective chemical formulations requires extensive laboratory work and a solid methodology. This paper reports a laboratory feasibility study, aiming at designing a surfactant-polymer pilot for a heavy oil field with an oil viscosity of ~500cP in the South of Sultanate of Oman, where polymer flooding has already been successfully trialed. A major driver was to design a simple chemical EOR method, to minimize the risk of operational issues (e.g. scaling) and ensure smooth logistics on the field. To that end, a dedicated alkaline-free and solvent-free surfactant polymer (SP) formulation has been designed, with its sole three components, polymer, surfactant and co-surfactant, being readily available industrial chemicals. This part of the work has been reported in a previous paper. A comprehensive set of oil recovery coreflood tests has then been carried out with two objectives: validate the intrinsic performances of the SP formulation in terms of residual oil mobilization and establish an optimal injection strategy to maximize oil recovery with minimal surfactant dosage. The 10 coreflood tests performed involved: Bentheimer sandstone, for baseline assessments on large plugs with minimized experimental uncertainties; homogeneous artificial sand and clays granular packs built to have representative mineralogical composition, for tuning of the injection parameters; native reservoir rock plugs, unstacked in order to avoid any bias, to validate the injection strategy in fully representative conditions. All surfactant injections were performed after long polymer injections, to mimic the operational conditions in the field. Under injection of "infinite" slugs of the SP formulation, all tests have led to tertiary recoveries of more than 88% of the remaining oil after waterflood with final oil saturations of less than 5%. When short slugs of SP formulation were injected, tertiary recoveries were larger than 70% ROIP with final oil saturations less than 10%. The final optimized test on a reservoir rock plug, which was selected after an extensive review of the petrophysical and mineralogical properties of the available reservoir cores, led to a tertiary recovery of 90% ROIP with a final oil saturation of 2%, after injection of 0.35 PV of SP formulation at 6 g/L total surfactant concentration, with surfactant losses of 0.14 mg-surfactant/g(rock). Further optimization will allow accelerating oil bank arrival and reducing the large PV of chase polymer needed to mobilize the liberated oil. An additional part of the work consisted in generating the parameters needed for reservoir scale simulation. This required dedicated laboratory assays and history matching simulations of which the results are presented and discussed. These outcomes validate, at lab scale, the feasibility of a surfactant polymer process for the heavy oil field investigated. As there has been no published field test of SP injection in heavy oil, this work may also open the way to a new range of field applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".