Design of a Robust ASP Formulation for Clay Rich and Moderate Permeability Sandstone Reservoir: From Laboratory to Single Well Chemical Tracer Test in the Field
Bibliographic record
Abstract
Abstract Sandstone reservoirs containing significant amount of clays (30-40 wt%) with moderate permeability (20-50 mD) provide a unique challenge to surfactant based enhanced oil recovery (EOR) processes. A critical risk factor for these types of reservoirs is adsorption of surfactants due to greater surface area attributed to clays. Clays also have high cation exchange capacity (CEC) and can release significant amounts of di-valents that lead to increased retention of the surfactant. These factors could adversely affect the economics of a flood. We present a case study where a robust formulation was designed and tested in lab/field for a reservoir located in Wyoming, USA and contains up to 35-40 wt% clays (predominately Kaolinite and Illite). The residual oil saturation is high (Sor=0.4) while the permeability of the formation is between 20-50 mD. The reservoir has been waterflooded historically with low salinity water which has led to formation permeability damage. Due to high levels of clays, adsorption of the surfactant on the rock surface was determined to be between 3-4 mg/g rock by static adsorption tests. This publication demonstrates how the following challenges have been successfully addressed in the lab as well as in the field in the form of single well chemical tracer test (SWCTT). Designed a robust alkaline-surfactant-polymer (ASP) formulation that showed ultra-low interfacial tension (IFT) values and aqueous solubility remains soluble in the aqueous solution over a broad range of salinity. Mitigated surfactant adsorption issues to make the cEOR solution economic. A sacrificial agent was identified that acted synergistically with alkali and also did not alter the optimum salinity of the formulation. Performed restored state core analysis using the available damaged core material. The main challenge being restoration of the coreplugs to current reservoir conditions for coreflood experiment without causing additional formation damage due to injection of low salinity formation brine. Designed a flood that utilized a pre-flush to provide a favorable salinity gradient and to inject sacrificial agent ahead of the surfactant front. Performed polymer screening to select right molecular weight of polymer so that the right balance of mobility control and injectivity in the reservoir can be obtained.
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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 | 0.000 | 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 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".