Bibliographic record
Abstract
Abstract Foamy oil solution gas drive mechanisms are complex and our knowledge and understanding is limited despite extensive studies in the literature. In order to advance our understanding of heavy oil solution gas drive mechanisms, long core depletion experiments were designed. These experiments were performed on sand-filled or glass bead-filled tubes that are x-ray transparent and have pressure transducers along their length. The novelty of the experiments is the length that they extend (over 18 m) and the duration of the experimental runs. The results of the longer experiments should be able to provide data that bridge the gap between the field scale and the shorter laboratory experiments that have been performed in the past. Thus, production, pressure transient and saturation data are presented in this 'extended' scale. In ddition, CT scanner images are expected to provide information about the evolution of gas. Introduction Cold production, or more elaborately, Cold Heavy Oil Production with Sand (CHOPS), has been tried in unconsolidated or weakly consolidated sands as a non-thermal stimulation process in which both sand and oil are produced together in order to enhance oil recovery. The oil production process is also typified by the formation of a so-called foam(1) as a result of gas exsolution and dispersion of tiny gas bubbles with limited growth in size. An intriguing observation is that the resulting foamy oil flow seems to greatly enhance oil production rates with high primary recovery factors despite the high oil viscosity. There have been many explanations put forward for interpreting such a phenomenon; namely sand production, retardation of reservoir pressure decline, enhancement of absolute permeability and high critical gas saturation. This paper looks into some of the above-mentioned issues by exploring numerically the inter-relationship between sand production, sand failure and foamy oil flow during the enhancement of oil production in a non-thermal process such as CHOPS. The Model There has been a series of papers published by the authors on the topic of sand production modelling(2–4) in conjunction with geomechanical issues and, recently, foamy-oil flow(5–7). These form the basis and frame of reference for the modelling effort reported in this paper. The avid reader seeking details of formulation and computer implementation is thus directed to the above-mentioned references. In view of providing some background for the subsequent discussions, the main features of the sand production, foamy oil flow and geomechanics models are summarized in the next subsections. Formulation Basically, we are faced with a porous medium which is multiphasic in character consisting of gas (bubbles), oil, fluidized solids and solid phases. The oil contains dissolved gas which is liberated as the pressure drops below bubble point through gas exsolution, and thereby enters into the gas phase. For continuum mechanics modelling purposes, all above-mentioned phases are homogenized through a mathematical artifice within the theory of mixtures(8) (see Figure 1). As such, mass balance equations can be written for each phase, and thereafter supplemented with two constitutive equations to FIGURE 1: Representative Element Volume: discontinuous phases and homogenization. Available in Full Paper
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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) | 0.002 | 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".