A Simplified Methodology on Selection, Operation, and Optimum Design of Steam Drive Reservoirs
Bibliographic record
Abstract
Abstract Steam drive is a viable, proven technology for the development of heavy oil reservoirs. Prior to implementing the steam drive process, a prescreening strategy to evaluate the recovery potential is valuable. Meanwhile, the effect of operating conditions, the design techniques to satisfy specific operating conditions, and the management of any subsequent infill drilling programs in light of the steam drive history are all critical to successful steam drive projects. Based on numerical simulation and field cases, this paper provides some critical strategies and a simplified methodology for evaluating the steam drive process. Based on reservoir physical properties, a pre-screening formula is proposed for computing recovery factors that allow preliminary economic analyses to be performed. It is shown that a successful steam drive project must satisfy the following four operating criteria:Steam injection rate is 1.6 to 1.8 m3/(d.ha.m);Production injection ratio is greater than or equal to 1.2;Bottom hole steam quality is greater than 40% and,Reservoir fluid pressure is maintained below 5 MPa. A design methodology is also provided, which can provide information on the most effective well pattern (5-spot, inverted 7-spot, and inverted 9-spot) and well spacing in order to satisfy the above four criteria. The sensitivity of the methodology to the injectivity/productivity estimates is also discussed. Introduction The steam drive process is an effective technology to develop heavy oil reservoirs. Most steam drive projects operated over the last two decades have achieved satisfactory oil recovery relative to their reservoir properties. From the late 1980s to the early 1990s, more than 10 steam drive pilots were operated in China. Unfortunately, most of them did not achieve their anticipated recovery factors for the following reasons(1):Some key parameters assumed prior to initiating the pilot were not realistic. For example, the steam drive pilot of Du I Group in Du 163 Block applied 5-spot 4 ha well patterns and a steam injection rate of 140 m3/d-CWE. However, the liquid production capacity of a single well was only 40 – 50 m3/d. This low production/injection ratio resulted in poor pilot performance.Some heavy oil reservoirs were simply not suitable for the steam drive process. For example, Well Group 3 –4 –032 and Well Group 3 –4 –76 in Gaosheng oilfield are over 1,600 m deep. At this depth, the bottomhole steam quality remained well below the target of 60% due to excessive heat loss in the injection tubing and the high reservoir pressure.Ineffective operation of the field pilots. The productivity of some pilots was reduced by formation contamination during drilling and completion. In some pilots, the liquid level in production wells was very high indicating poor production optimization and, in most cases, the steam quality was too low to achieve high recovery factors. Based on a theoretical study and field case studies, this paper provides critical suggestions on the following four aspects of the steam drive process:What types of heavy oil reservoirs are suitable for a steam drive project?
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".