Recent Advances in Seismic Monitoring of Thermal EOR
Bibliographic record
Abstract
Abstract Well-planned and executed reservoir surveillance has proven to add significantly to the production and ultimate recovery of hydrocarbons, notably in areas of Improved and Enhanced Oil Recovery (IOR/EOR). Recent technological advances in the area of data acquisition and integration have led to increased use of well and reservoir surveillance data to optimize such processes. In the case of thermal EOR, one of the most important subsurface uncertainties impacting performance is heat and steam front conformance, both vertically and arealy. This paper illustrates new geophysical technologies used for monitoring various thermal EOR recovery strategies in The Netherlands, Canada, and Oman. We focus on permanently buried seismic sources and receivers, refraction seismic, down-hole seismic, and the newly developed Distributed Acoustic Sensing (DAS) to enable low-cost and non-intrusive seismic surveillance. These technologies are not without challenges, but our field trials indicate they have the potential to broaden the successful application of reservoir monitoring onshore. Introduction Enhanced Oil Recovery (EOR) is well established as a tool for increasing recovery beyond secondary production, extending the life of fields, and postponing field abandonment activities and costs. Thermal EOR in particular has been instrumental to unlocking resources that may not be producible otherwise (e.g., large bitumen resources in Canada), and increasing recovery from heavy oil fields that have already undergone waterflood. Currently, thermal EOR is done only onshore, and for relatively shallow reservoirs (e.g., up to 1 km depth - a limitation imposed by heat losses that would condense steam to hot water for deeper injection wells). To optimize EOR, one must monitor reservoir changes induced by the treatment. Seismic monitoring is attractive for its ability to sense a large volume of the subsurface, illuminating it between and away from existing wells. It has proven useful for reservoir monitoring offshore (e.g., Hatchel et al., 2013; Stammeijer et al., 2013; El Ouair and Strønen, 2006; Osdal et al., 2006; Howie et al., 2005; Whitcombe et al., 2004). However, it has been slow to mature onshore, both because the Value-of-Information hurdle is higher in fields with high well density (typical for mature onshore developments) and because of a number of technical and non-technical challenges. The technical challenges include spatial and temporal variability of the near surface, changes in source and receiver coupling to the formation, complex and evolving infrastructure, high noise levels caused by activities above ground, and the relatively high cost of seismic operations in forested or populated areas. Non-technical challenges may include land access (e.g., restrictions due to competition with other uses such as farming or aboriginal activities), regulatory and environmental issues (surface footprint) and public perception of risk.
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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".