Successful Field Implementation of CO2-Foam Injection for Conformance Enhancement in the EVGSAU Field in the Permian Basin
Bibliographic record
Abstract
Abstract This paper presents the performance of a CO2 foam injection pilot implemented in the East Vacuum Grayburg San Andres Unit (EVGSAU) by ConocoPhillips in cooperation with The Dow Chemical Company. The pilot project focuses on a single CO2 injection pattern, consisting of one injector and eight producers, selected due to signs of early gas breakthrough and poor overall sweep efficiency. To solve these conformance issues and increase overall pattern production performance, a new foaming surfactant with low adsorption and high gas partitioning characteristics was developed and experimentally tested at simulated reservoir conditions. A "water alternating surfactant-in-gas" injection strategy was created utilizing a history matched reservoir simulation model and an empirical foam model. This reservoir model was also utilized to better understand the dependency of surfactant concentration on foam generation and fluid diversion. Injection profile logs (IPLs) were also run, in both water and CO2 phases, prior to pilot implementation to establish baseline injection performance. This paper will detail several performance indicators that illustrate sustained improvement in pattern injection and production after more than 15 cycles of alternating water, CO2+surfactant, and CO2-only injection. During each cycle, gas injectivity trends were calculated and compared to the baseline to monitor foam strength and performance. Four additional IPLs were run, which indicated continuous improvement in vertical sweep efficiency and ultimately resulted in uniform injection distribution between the upper and lower sections of the producing zone. Finally, the most significant result of the trial was the uplift in pattern oil production. It has averaged ~20% above the baseline production forecast throughout the entire pilot period and peaked within the first six months at ~60% above the baseline. The success of this pilot illustrates the benefits of using a low adsorbing and CO2 soluble foaming surfactant to address reservoir conformance issues for CO2 floods. Further optimization of the pilot based on the simulation forecast is planned to improve long-term pilot economics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".