Bibliographic record
Abstract
Abstract California has one of the largest reserves for heavy oil in the world, second only to Venezuela. Recent declines in conventional resources and reserves during the last decade have prompted other jurisdictions to examine their prospective unconventional resources, such as heavy oil and oil sands, in a more favorable technological and economic setting. However, this has not been done universally in the United States, where thermal enhanced oil-recovery technologies (mostly used to produce heavy oil) have experienced a decline in production, concomitant with the downturn in conventional production. In California, the seep and oil-sand deposits are mostly unconsolidated sands bound together by biodegraded bitumen. Source rocks for both modern seeps and oil sands and ancient heavy-oil deposits are mainly the Miocene Monterey diatomites and equivalent diatomaceous mudstones and organic shales. In California, most of the seeps and oil sands overlie or are updip from underlying heavy-oil reservoirs. The seep and oil-sand deposits occur in areas where cap-rock integrity was compromised for the underlying heavy-oil reservoirs, breeched mainly by faults or fractures. Hydrocarbons migrated updip into basin-marginal settings or in structural areas of compromised cap rock and then pooled to form the seeps, later hardening into oil-sand deposits. Hydrocarbons accumulated in a wide variety of depositional environments from deep-sea fans, lobes, and submarine channels to fluvial-lacustrine deltas and incised valleys and every other sedimentary environment in between. This makes it difficult to identify type examples for the California accumulations, although case examples are given. In the past, steam-flood, condensed water drive, cyclic steam stimulation (CSS), and fireflood were used to produce the California heavy-oil reservoirs. Currently, significant California CSS projects underway include Belridge, Cymric, and northern Midway Sunset fields to stimulate intermediate-gravity hydrocarbons in the Monterey, Reef Ridge, and Etchegoin diatomite lithologies. Elsewhere, for example, in Canada, in-situ bitumen and extra-heavy-oil sands are commonly developed using CSS or steam-assisted gravity drainage (SAGD). Combined application of CSS along with SAGD from horizontal wells may recover bypassed pay in heavy-oil reservoirs and may be used to recover bitumen from associated oil sands, and multistage multifracing technologies may recover oil from the deeper unconventional (Monterey) source rocks. These technological developments, along with improved computing techniques (i.e., three-dimensional [3-D] geologic modeling/visualization), allow for real-time exploration and development of unconventional reservoirs. A significant effort exists in California to improve recovery from Pleistocene, Pliocene, and Miocene heavy-oil deposits; for example, at present, 70 to 80% recovery from heavy-oil steam drives is seen in Pleistocene Tulare Formation fluvial and alluvial sands. Full 3-D models anchored by extensive coring and logging programs have reaped benefits in many older oil fields (e.g., South Belridge, Midway Sunset, Cymric, Lost Hills, Kern River). Bypassed pay and new production from associated shallow oil sands and deeper source rocks may ultimately be a key to attainment of increases in secure unconventional energy reserves in North America. In the future, full integration of new technologies, along with technology sequencing, may be applied to old California oil fields for production of bypassed pay in heavy-oil fields.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".