Experimental Study of Surfactant Polymer for a Heavy Oil Field in South of Oman
Bibliographic record
Abstract
Abstract Heavy oil reservoirs remain challenging for surfactant-based EOR, particularly in selecting fine-tuned chemical formulations which combine high performance and guarantee trouble-free operations. This requires substantial 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 in the South of Oman. The workflow was organized as follows: (i) oilfield initial assessments; (ii) selection and preparation of the rock and fluids; (iii) initial design of the surfactant-polymer formulation involving an extensive screening of surfactants and polymer combinations using a robotic platform; (iv) pre-qualification tests involving in-vitro adsorption and assays for emulsion risk at surface conditions; (v) demonstration coreflood test aiming at determining the ultimate oil recovery achievable with the formulation and (vi) optimization study involving coreflood test and formulation fine tuning to design the most adapted injection strategy. An extensive screening study was carried out to select representative core and fluid materials. To enable comparative coreflood tests, an analogue granular porous medium was built, with mineralogical composition mimicking that of the reservoir rock. The design of the surfactant-polymer formulation relied on hundreds of automated salinity screening phase behavior and solubility assays. Polymer selection was achieved by membrane injection tests performed under imposed velocity conditions to determine resistance factors versus pore size, velocity, concentration and salinity. This procedure resulted in the successful design of a surfactant-polymer formulation that provided o/w IFT of less than 10-2 mN/m at 50°C over a relatively broad range of salinities, including the injection water salinity. A first coreflood test performed on a reservoir rock plug demonstrated that injecting the formulation as an infinite slug in post-polymer flooding conditions led to 100% recovery of the remaining oil with a very good in-depth propagation of the formulation. The optimization study was then carried out and led to designing injection sequences that allowed minimizing the surfactant losses due to both retention on the rock and dissolution in oil. The results demonstrate that a surfactant-polymer formulation can successfully be designed and evaluated for heavy oil reservoirs. They also provide practical guidelines for the pilot implementation and pave the way for the next stage of the feasibility study which will focus on generating data for reservoir simulation, operational design and improving the economics.
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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) | 0.000 | 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".