Characterization of Operative Mechanisms in Gravity Drainage Field Projects Through Dimensional Analysis
Bibliographic record
Abstract
Abstract The Gas-Assisted Gravity Drainage (GAGD) process is being developed through a joint effort between the U.S. DOE and LSU, to examine the effectiveness of improving gravity drainage of oil to horizontal producers by injecting gas through existing vertical wells. To facilitate fair and effective performance comparisons between the conventional water-alternating-gas (WAG) and the GAGD process, and decipher the controlling operational multiphase mechanisms in gas injection EOR processes, the dimensional analysis approach was employed. Nine gravity stable and eight WAG field applications in the U.S., Canada, and rest of the World were studied for this purpose. A newly defined ‘index of productivity’ and five dimensionless groups, namely Capillary (NC), Bond (NB), Dombrowski-Brownell (NDB), Gravity (NG), and Grattoni et al.'s ‘N’ group were calculated for these gravity stable field projects. Dimensional analysis results for all gravity drainage field projects indicated that these dimensionless numbers can be classified into two groups:petrophysical parameter(s) dependent groups: NB, NDB and N, andoperational parameter(s) dependent groups: NC and NG. NC and NB groups effectively envelope the interplay of the dominant reservoir forces, namely gravity, viscous, and capillary forces. These groups coupled with the microscopic Bond number (NDB) aid in characterizing the flow regimes and governing forces in the field as well as laboratory displacements. The NG and N groups provide useful augmentation for scale-up and displacement characterizations. This paper provides the results of step-by-step dimensional analyses for all the field cases studied and attempts to characterize the controlling multiphase mechanisms in gas injection EOR processes. Additionally, this work attempts to characterize the fluid dynamics associated with gravity drainage processes through existing (namely, capillary number, bond number, gravity number etc.) and newly defined (" gravity drainage" number) dimensionless groups. 1. Introduction 1.1 Background The stranded oil resources - "EOR Prize" - left-behind after primary and secondary recovery processes total nearly 400 billion barrels(1–4) in the United States alone, and have been estimated to top nearly 2 trillion barrels world-wide(2,4). Although this oil has been deemed to be unrecoverable by current technology, we cannot afford to walk-away from this already discovered, enormous resource base. The increased need for energy self-reliance in the new millennium, as well as the recent record high-crude oil prices have further intensified the need for ‘enhanced recovery’ from these known-to-exist reserves. 1.2 Growth of Gas Injection EOR As of the latest (2006) EOR survey published biannually by the Oil and Gas Journal, gas injection has become the largest EOR process in the U.S., displacing the long reigning thermal processes. Enhanced Oil Recovery (EOR) activities in the United States account for nearly 13% of the U.S. domestic production (Feb 2006)(3), and their importance as well as contributions continue to rise. The major processes contributing nearly 98% of the U.S. EOR oil are:thermal methods (used in heavy oil production) (46.5%),CO2 injection (mostly miscible) (36.5%) andhydrocarbon (HC) gas injection (14.8%). The changes in the U.S. EOR application and distribution scenario from 1986 to 2006 are shown in Figure 1(2). Figure 2(2,4) shows the dynamics of the various gas injection EOR processes; the current U.S. dominant EOR method in the U.S.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".