Acidic Steam Modeling and Simulation for Heavy Oil and Extra Heavy Oil Reservoirs
Bibliographic record
Abstract
Introduction Over the past three decades, the decline in reserves of conventional crude oil has led to the development of several methods in order to enhance oil recovery for heavy oil deposits. Globally, heavy oil accounts for approximately 50% of hydrocarbon volume in place (Ehlig-Economides et al., 2000). There exist sixteen major oil sands deposits all over the world. As a matter of fact, the two largest are the Athabasca oil sands in Northern Alberta and the Orionco - River deposit in Venezuela. By comparison, the Athabasca oil sands alone cover an area of more than 42000 km2, in which oil storage is more than all the known reserves in Saudi Arabia. It is found that only one sixth of over 1.7 trillion barrels of heavy oil are recoverable with current technologies. These technologies include mining, thermal recovery, cold production and etc. Mining only makes economic and engineering sense when the depth of overburden is less than about 75 meters. Hence, only about 10 - 20% of the oil sands can be mined. As a result, recovery of the remaining 80 - 90% of the oil sands depends on the so-called thermal-recovery process, which basically depends on using energy to produce energy. In order to begin to separate the heavy oil from the sand/carbonates, deposits have to be heated to lower the viscosity of the heavy oil. One of these thermal recovery methods is the Solvent Assisted Process (SAP) that appears tremendously successful, especially for bitumen. SAP process involves injection of solvent and steam in several wells. Even though the injector well and producer can be very close, the mechanism of SAP causes a growing steam saturated zone, known as the steam chamber, to expand gradually and eventually allow drainage from a very large volume. Both field and numerical simulation studies have demonstrated the success of SAP drainage. The prediction of SAP performance by numerical simulation is an integral component in the design and management of a SAP project. In this regard, the solvent is chosen to be Acid in this study. In order to inject steam with solvent (acid), two separate chambers of acid and steam are heated up to a certain degree on the surface. Steam only is injected into the well. The well is shut in for 2 hrs and then put on production in which acid is mixed with steam and injected together after cleanup period. Conventional reservoir modeling approach computes multiphase flow in porous media but generally does not take the geomechanical effects into account. Unfortunately, this assumption is not valid for oil sands, because of their high sensitivity on pore pressure and temperature variations but can be applied in carbonate formations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".