Direct Current Electrical Enhanced Oil Recovery in Heavy-Oil Reservoirs To Improve Recovery, Reduce Water Cut, and Reduce H2S Production While Increasing API Gravity
Bibliographic record
Abstract
Abstract Electro-Petroleum, Inc. (EPI) has successfully demonstrated use of DC electrical current for enhanced oil recovery (a process we now call "Electro-Enhanced Oil Recovery", or EEOR) at heavy oil fields in the Santa Maria (California) Basin and the Eastern Alberta Plains. They have also conducted large-scale (1 cu-m sample size) laboratory studies to evaluate unexpected results from these field demonstrations. Dr. G. V. Chilingar, of the University of Southern California, publicly advocated using a similar technology in the 1960’s. Several successful applications of similar technology have been claimed, in the Former Soviet Union. Field studies in California have demonstrated up to a ten-fold increase in oil production from a field containing 8° API gravity oil. Recent research has demonstrated that the EEOR process is also capable of cold cracking of heavy oil in-situ, resulting in lighter produced oil and increased reservoir pressures. EEOR can function at depths below 10,000 feet, well below the 2,500 ft. practical limit for steam flood operations. Observed changes in the produced fluids include:Increased oil production rates.Reduced produced oil viscosity increased API gravity).Reduced water cut.Reduced H2S production.Increased gas production.Increased produced gas energy (heavy gas) content.Reduced PAH production. All of these observed fluid chemistry changes can be explained by the use of DC electrical technology.
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 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 | 0.000 | 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) | 0.001 | 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".