Well Performance Analysis for Heavy Oil With Water Coning
Bibliographic record
Abstract
Abstract As conventional petroleum is approaching its maximum production and the world oil demand continues to grow, heavy oil becomes one of the obvious replacement resources. By 2015, its contributions to total oil production would reportedly grow from the present 2.5 MMbpd to 4MMbpd and stay at this level for a couple of decades. Recovery of heavy oil reservoir with wells is a challenge due to low API gravities (6 °-25 °), high viscosity (100cp-1000cp)-particularly in the presence of water. For example, the recovery factor from a heavy oil reservoir with bottom water in the H.K. oilfield, Shandong province, in China, having viscosity of 710 cp does not exceed one percent. One of the most important problems in heavy oil recovery is dramatic loss of wells' productivity at the onset of water inflow due to the two fluids' mobility contrast. Not only the recovery at breakthrough time is very low, but also the water cut increase is extremely rapid. The presented simulation study investigates dynamics of productivity loss in wells producing heavy oil with bottom water. The production system (nodal) analysis model simulates inflow performance relationship with variable water cut. The model captures the difference between heavy and light oil in terms of mobility ratio effect, recovery dynamics prior to and after water breakthrough, and water cut control with production rate. The results show that preventing water breakthrough to wells in heavy oil is several-fold more important (in terms of well productivity and recovery rate) than that for conventional oil wells. Introduction Definition of heavy oil is not rigorous and varies between authors. Some clarify heavy oil by density measured on the API gravity scale as lower than 20 API. Others emphasize in-situ viscosity of heavy oil. Conventional-oil viscosity may range from 1 cp to about 10 cp. Viscosity of heavy oil and extra heavy oils may range from less than 20 cp to more than 1,000,000 cp. On the extreme, the most viscous hydrocarbon, bitumen, is a solid at room temperature (1, 2). Most of the world's oil resources are heavy, viscous hydrocarbons. It is commonly accepted that after conventional oil and natural gas, the next easiest fossil fuel resource to develop is the viscous oil. It has been estimated that there is probably 2.5 times the amount of viscous oil as there is conventional oil. By some estimates, there are 8–9 trillion barrels of heavy oil and bitumen in place in the world (not including hydrocarbon in shales) (1). Canada has the largest heavy oil resource with some 1.7 trillion barrels of extra-heavy oil situated in the oil sands of Alberta, plus a further 25 billion barrels of heavy oil in the 10 – 22.3 API gravity range. Venezuela has around 1.2 trillion barrels of extra-heavy oil in the 400-mile long Orinoco Belt in the eastern part of the country. Reserves in Russia-another heavy oil giant, are approaching 200 billion barrels of bitumen and extra heavy oil (1- 3).
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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".