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Record W4400193574 · doi:10.2118/0724-0102-jpt

Study Examines Limitations of CCS and Their Effect on Oil and Gas Production

2024· article· en· W4400193574 on OpenAlexaboutno aff
Chris Carpenter

Bibliographic record

VenueJournal of Petroleum Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon capture and storage (timeline)Production (economics)SubsidyTerminologyEnhanced oil recoveryFossil fuelNatural resource economicsEnvironmental economicsEnvironmental scienceBusinessWaste managementEngineeringEconomicsPolitical scienceClimate changeLaw

Abstract

fetched live from OpenAlex

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214950, “Limitations and Fallacies of Carbon Capture and Storage and Impact on Oil and Gas Production,” by S.M. Farouq Ali, SPE, and Mohamed Y. Soliman, SPE, University of Houston. The paper has not been peer reviewed. _ In the complete paper, the authors write that, while carbon capture and storage (CCS) initiatives are affecting oil and gas operations profoundly, such efforts have had little perceptible effect on atmospheric CO2, which continues to increase. The paper aims to show that current CCS regimens have serious technical and fiscal constraints and questionable validity, stating that, globally, CCS has not increased beyond approximately 0.1% of global CO2 emissions in the past 20 years. The paper offers partial solutions and concludes that, while oil and gas will continue to be important energy sources beyond the foreseeable future, oil companies will accomplish the needed CCS. Introduction The authors write that, while CCS efforts have been pursued for 4 decades, little has been achieved. For the past 20 years, the percentage of CO2 captured and stored is less than 0.1% of the CO2 emitted worldwide, if one considers CO2 enhanced oil recovery (EOR) projects to be CSS—which, the authors write, is a fallacy. They emphasize that CCS means injection with no production. The key to CCS success, they write, is major governmental subsidization, by whatever terminology it is known, and that means taxpayer money. Sweeping decisions that have a profound effect on oil and gas production and petroleum engineering education are being made based on predictions of an increase in CO2 concentration in the atmosphere in various time frames. Magnitude of the Problem The problem of world CO2 emissions capture is gigantic. To appreciate the magnitude of the problem, imagine that 1 year’s CO2 emissions (40 billion tonnes) are captured, compressed and liquified, and injected into a reservoir the size of the Ghawar oil field, the largest reservoir in the world, with the entire pore space (approximately 0.5 Tcf) available for storage. In this hypothetical, nine such reservoirs would be required every year. Presumably, such storage space can be found, but collecting the CO2 and bringing it to a storage site is a highly complex task. For example, in a sequestration effort in a building complex in New York, the CO2 is separated, liquified, and trucked to a storage site to be injected underground, which is impractical. Often, the example of the Nordic countries (mainly Denmark, Sweden, and Norway) is cited as evidence of successful emissions reduction. But the total population of these countries is approximately the same as that of metropolitan Mumbai in India. Definitions Carbon capture use and storage (CCUS) implies that the CO2 produced by various processes is captured and used for EOR. This accounts for approximately 30% of the 230 mtpa of CO2 captured globally. CCS means that any CO2 produced by the process is captured and injected into a suitable geological formation for storage for thousands of years. Net-zero emissions (NZE) implies that all the CO2 produced is captured and stored. In Canada, “absolute zero emissions” is discussed, meaning that no CO2 is produced in the first place, pointing to total fossil-fuel phaseout.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.266
Teacher spread0.244 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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