Successful CO2-Foam Field Implementation for Improving Oil Sweep Efficiency in EVGSAU Field at Permian Basin: Expansion Phase
Bibliographic record
Abstract
Abstract A successful CO2-foam technology has operated at the East Vacuum Grayburg San Andres Unit (EVGSAU) since Jan 2018 by ConocoPhillips in cooperation with Dow. In early 2020, scope of the technology was expanded from one pattern to three patterns. The expansion phase was implemented to evaluate scalability of this technology to patterns with diverse conformance issues and productivity inefficiencies. Severe vertical and areal conformance issues were initially identified in these patterns, resulting in early gas breakthroughs and poor oil sweep efficiencies. Due to the outstanding performance of the first phase, the same surfactant with high foaming tendencies, high gas solubility, and low adsorption characteristics was implemented in the new patterns. In contrast to the first foam pattern, gas injectivity was reduced by 20 to 50% after only 2 foam cycles in the new patterns. Based on injection profile logs (IPL), no out-of-zone injection was identified before the surfactant injection for the two new patterns, which can be the reason for such rapid injectivity responses. Similar to the first pattern, deep conformance corrections were confirmed as gas was redirected from highly connected producers to other producers within the new patterns. A lower surfactant dosage was applied to one of the new patterns to optimize chemical consumption, while sustaining the performance. The surfactant concentration was also reduced in the first pattern to study the effect of a lower dosage on a known performing pattern. During the foam implementation, the gas to water ratio (GWR) at the pattern injectors was increased to maintain the patterns at the baseline fluid throughput. This adjustment resulted in more than a 50% reduction in water consumption and a 17% improvement in gas utilization. Overall, a sustainable increase in oil production rate (30 to 40% over the baseline for the last two years) was achieved in the three foam patterns as a result of the foam implementation. This three-pattern CO2-foam field result is an outstanding example of how proper implementation of a novel surfactant in a conventional reservoir with mild-to-severe deep conformance issues can improve oil sweep efficiency. The application of this foam technology has demonstrated the extension of the life of a mature asset like EVGSAU by arresting the historical decline in the oil production rate. Reduction in energy and water consumption per barrel of oil produced, and further CO2 sequestration are other benefits of this technology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".