Interfacial Tensions and Visual Interactions of Crude Oil-Brine-CO Systems Under Reservoir Conditions
Bibliographic record
Abstract
Abstract In this paper, an experimental technique is developed to study the interfacial tension phenomenon and visual interactions of crude oil-brine-CO2 systems at different pressures and temperatures. The major component of this experimental set-up is a see-through windowed high-pressure cell. By using the axisymmetric drop shape analysis (ADSA) for the pendant drop case, this new technique makes it possible to determine the interfacial tension (IFT) and to visualize the interfacial interactions among crude oil, brine and CO2 under practical reservoir conditions. More specifically, IFT of the crude oil-brine-CO2 system is measured as a function of pressure and temperature, respectively. For the crude oil-CO2 system, it is found that the dynamic IFT gradually reduces to a constant value, i.e., the equilibrium IFT. Meanwhile, a number of important physical phenomena are observed after the crude oil is made in contact with CO2. In particular, the oil swelling effect, light-ends extraction, initial turbulent mixing and wettability alteration are the major characteristics of the CO2 flooding processes. There always exists a constant low IFT (i.e., partial miscibility) as long as the pressure is higher than a threshold value. No ultra low or zero IFT between the crude oil and CO2 is found, regardless of the operating pressures and temperatures tested in this study. For the crude oil-brine-CO2 systems, wettability between crude oil and needle surrounded by CO2-saturated brine phase is different from that of the crude oil-CO2 systems. In addition, immiscibility between CO2- saturated crude oil and CO2-saturated brine is still observed at P=28.196 MPa and T= 58?C. Therefore, this laboratory study shows that partial miscibility between the crude oil and CO2 occurs in the reservoirs and that wettability alteration may considerably improve the oil recovery in a water-wet reservoir during CO2 flooding processes. Introduction CO2 flooding is considered to be one of the most promising enhanced oil recovery (EOR) techniques because it not only effectively enhances oil recovery but also considerably reduces greenhouse gas emissions. There have been extensive laboratory studies and field applications of CO2 EOR processes in the past five decades. It has been found that these processes can enhance oil recovery normally by up to 8–16% of the original oil in place[1, 2]. In the CO2 flooding processes, saturation distribution and flow behavior of crude oil, gas and brine is controlled largely by the interfacial interactions among crude oil, reservoir brine, CO2 and reservoir rocks, such as interfacial tension (IFT), wettability, capillarity and dispersion[3]. Also, field applications show that early breakthrough, unstable fronts and injectivity loss are three common major problems encountered in the CO2 EOR processes[1]. Physically, these three unresolved technical problems are closely related to the interfacial properties of crude oil-fluid-rock systems in the reservoirs. Therefore, it is important to accurately describe the interfacial interactions of the crude oil-fluid-reservoir rock systems with dissolution of solvents, such as CO2, under reservoir conditions. In general, the ultimate oil recovery in the CO2 flooding processes is dependent on the oil viscosity reduction, oil swelling effect and changes of interfacial properties between crude oil and CO2[1, 4, 5–7].
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".