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Record W1969769356 · doi:10.2118/2004-201

Solution Gas Production Profiling

2004· article· en· W1969769356 on OpenAlexaboutno aff
Donald Béliveau

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsProfiling (computer programming)Computer scienceProduction (economics)Operating system

Abstract

fetched live from OpenAlex

Abstract As our industry focuses more on natural gas production and on wringing the last drop of value from each property, it has become increasingly important to properly characterize and predict the solution gas performance from our oilfields. This paper will review historical techniques for predicting solution gas production under various common process mechanisms including depletion, weak and strong water drive, gas-cap drive, and production of a volatile oil. The paper will also discuss situations where numerical simulation may be required instead of using standard analytical techniques. The paper will present examples from the Western Canadian Sedimentary Basin that illustrate solution gas performance under each of the drive mechanisms mentioned above. Results are displayed using the "equal-value" concept, which shows the progress of each oilfield from its early life where revenue is dominated by oil sales to its later life when revenue becomes increasingly dominated by solution gas production. Introduction to the Problem The prediction of solution gas production is often taken for granted in oilfield forecasts, but is a multi-faceted problem when one considers the many components that impact the process (fluid and rock properties, interactions between fluids/rocks, geological properties, drive mechanisms, wellbore conditions, etc). Years ago, accurate predictions of solution gas volumes were less important because there was little or no value associated with the product. Today, the value of natural gas is essentially the same as oil on a heating value basis, so much more attention is paid to the prediction of solution gas volumes. Further, environmental pressures are driving government and industry to conserve all produced gas; and obviously it is important to understand how much gas will be produced to ensure installation of the appropriate conservation scheme. On its face, solution gas prediction is a deceptively easy problem: pressure declines, gas evolves from the oil, and is produced. So, if one completely understands the production mechanism and can accurately predict future pressure decline, and understands the PVT properties that govern the release of solution gas, and knows the rock properties that govern the trapping and flow of gas, and has a good picture of the overall geological model, then it can be fairly simple to predict solution gas production. Further, the interplay between these factors can result in non-unique solutions. Since the primary focus is mostly on oil volumes, appropriate attention is not always paid to the variables that govern solution gas production. The most common analytical methods for predicting solution gas production were developed by Tarner (Ref. 1) and Muskat (Ref. 2). These methods use material balance principles and a dynamic producing GOR to predict reservoir performance at pressures when the gas saturation exceeds the critical gas saturation. However, a number of simplifying assumptions were made in these analytical treatments, including thin horizontal reservoirs with negligible gravity forces (ie. no gas percolation). Other analytical approaches have been proposed that mitigate concerns with the Tarner and Muskat methods; however, all analytical methods have their own limiting assumptions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.251
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2004
Admission routes1
Has abstractyes

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