Application of Time-Lapse Seismic Monitoring for the Control and Optimization of CO2 Enhanced Oil Recovery Operations
Bibliographic record
Abstract
This project, 'Application of Time-Lapse Seismic Monitoring for the Control and Optimization of CO{sub 2} Enhanced Oil Recovery Operations', investigated the potential for monitoring CO{sub 2} floods in carbonate reservoirs through the use of standard p-wave seismic data. This primarily involved the use of 4D seismic (time lapse seismic) in an attempt to observe and map the movement of the injected CO{sub 2} through a carbonate reservoir. The differences between certain seismic attributes, such as amplitude, were used for this purpose. This technique has recently been shown to be effective in CO{sub 2} monitoring in Enhanced Oil Recovery (EOR) projects, such as Weyborne. This study was conducted in the Charlton 30/31 field in the northern Michigan Basin, which is a Silurian pinnacle reef that completed its primary production in 1997 and was scheduled for enhanced oil recovery using injected CO{sub 2}. Prior to injection an initial 'Base' 3D survey was obtained over the field and was then processed and interpreted. CO{sub 2} injection within the main portion of the reef was conducted intermittently during 13 months starting in August 2005. During this time, 29,000 tons of CO{sub 2} was injected into the Guelph formation, historically known as the Niagaran Brown formation. By September 2006, the reservoir pressure within the reef had risen to approximately 2000 lbs and oil and water production from the one producing well within the field had increased significantly. The determination of the reservoir's porosity distribution, a critical aspect of reservoir characterization and simulation, proved to be a significant portion of this project. In order to relate the differences observed between the seismic attributes seen on the multiple 3D seismic surveys and the actual location of the CO{sub 2}, a predictive reservoir simulation model was developed based on seismic attributes obtained from the base 3D seismic survey and available well data. This simulation predicted that the CO{sub 2} injected into the reef would remain in the northern portion of the field. Two new wells, the State Charlton 4-30 and the Larsen 3-31, were drilled into the field in 2006 and 2008 respectively and supported this assessment. A second (or 'Monitor') 3D seismic survey was acquired during September 2007 over most of the field and duplicated the first (Base) survey, as much as possible. However, as the simulation and new well data available at that time indicated that the CO{sub 2} was concentrated in the northern portion of the field, the second seismic survey was not acquired over the extreme southern end of the area covered by the original (or Base) 3D survey. Basic processing was performed on the second 3D seismic survey and, finally, 4D processing methods were applied to both the Base and the Monitor surveys. In addition to this 3D data, a shear wave seismic data set was obtained at the same time. Interpretation of the 4D seismic data indicated that a significant amplitude change, not attributable to differences in acquisition or processing, existed at the locations within the reef predicted by the reservoir simulation. The reservoir simulation was based on the porosity distribution obtained from seismic attributes from the Base 3D survey. Using this validated reservoir simulation the location of oil within the reef at the time the Monitor survey was obtained and recommendations made for the drilling of additional EOR wells. The economic impact of this project has been estimated in terms of both enhanced oil recovery and CO{sub 2} sequestration potential. In the northern Michigan Basin alone, the Niagaran reef play is comprised of over 700 Niagaran reefs with reservoirs already depleted by primary production. Potentially there is over 1 billion bbls of oil (original oil in place minus primary recovery) remains in the reefs in Michigan, much of which could be more efficiently mobilized utilizing techniques similar to those employed in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".