Mechanisms and Application of Viscosity Reducer and CO2-Assisted Steam Stimulation for a Deep Ultra-Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract Steam stimulation is an essential in-situ technology being used today to improve oil recovery from heavy oil reservoirs, it can be achieved through continuous or cyclic (huff-and-puff) injection or steam assisted gravity drainage (SAGD) process. Although steam injection process associated with horizontal wells(e.g. SAGD) has been successfully applied to improve the oil recovery in heavy oil reservoirs, reservoir depth and minimum pay zone thickness limitations still exist which restrict their application in deep reservoirs with a thickness less than 10 m. In addition, ultra-heavy oil viscosity (> 12×104 mPa•s at a reservoir temperature of 68~71 °C) challenges the conventional thermal recovery methods in such deep thinly laminated formations. In this study, a novel hybrid technology is proposed on the basis of combining the steam injection process with the viscosity reducer and CO2 injection to improve the ultra-heavy oil recovery in a deep thinly laminated reservoir. The improved oil recovery mechanisms for hybrid methods are experimentally studied through physicochemical characterization of ultra-heavy oil, viscosity reducer, CO2, and steam multisystem mixtures. More specifically, the viscosity, SARA (saturate, aromatic, resin, and asphaltene) content, molecular weight, aromaticity, and asphaltene structure parameters of five different multisystem mixtures are determined through a magnetic stirring autoclave and a viscosimeter, SARA analysis, molecular-weight measurements, and nuclear magnetic resonance (NMR) spectrometer, respectively. In addition, a total of 16 core flooding experiments are carried out to thoroughly study the performance of steam stimulation associated with viscosity reducer and CO2 injection in ultra-heavy oil formation. Orthogonal array technique is applied to determine the optimum injection volume of steam, viscosity reducer, and CO2. Furthermore, the performance of application of hybrid methods in Zheng 411 ultra-heavy oil reservoirs of Shengli Oilfield is evaluated. The viscosity reduction caused by adding oil-soluble viscosity reducer and CO2 into the steam are particularly favorable for achieving a higher heavy oil recovery compared with pure steam injection process. It is found that 84.38% viscosity reduction ratio can be achieved when steam is injected into heavy oil together with viscosity reducer and CO2. Physicochemical characterization of mixtures proves that the viscosity reduction mechanisms for hybrid methods are synergetic effects, which combine the asphaltene decomposition caused by adding viscosity reducer with physical viscosity reduction mechanisms caused by CO2 and steam. In addition, the steam injection pressure can be significantly decreased through CO2 injection process. Experimentally, this study also discovers that the optimum injection volume for steam, viscosity reducer, and CO2 is 2.5 pore volume (PV), 1.5 wt%, and 0.2 PV, respectively. Slug injection is the optimum process for viscosity reducer/CO2/steam systems. The viscosity reducer and CO2-assisted steam huff and puff process has been successfully tested in a deep thinly laminated reservoirs in Shengli Oilfield.
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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 | 0.000 |
| 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) | 0.001 | 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".