MétaCan
Menu
Back to cohort
Record W1550913446 · doi:10.1306/13371587st643550

Overview of Heavy Oil, Seeps, and Oil (Tar) Sands, California

2013· book-chapter· en· W1550913446 on OpenAlexaffabout
Frances J. Hein

Bibliographic record

VenueAmerican Association of Petroleum Geologists eBooks · 2013
Typebook-chapter
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsCanada Energy Regulator
FundersNational Institute on AgingCalifornia Department of Conservation
KeywordsOil sandsEnvironmental sciencetar (computing)GeologyPetroleum engineeringOil spillMining engineeringArchaeologyGeographyAsphalt

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.239
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations12
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Association of Petroleum Geologists eBooksSame topicPetroleum Processing and AnalysisFrench-language works237,207