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Record W2621118673 · doi:10.1071/aj16047

A practical workflow for performance prediction of low permeability reservoirs

2017· article· en· W2621118673 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe APPEA Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsFoothills Medical CentrePetro-Canada
Fundersnot available
KeywordsWorkflowComputer sciencePortfolioReservoir engineeringScale (ratio)Industrial engineeringOperations researchEngineeringPetroleumGeologyDatabase

Abstract

fetched live from OpenAlex

The Society of Petroleum Evaluation Engineers (SPEE) recently released ‘Monograph 4 – estimating ultimate recovery of developed wells in low-permeability reservoirs’ (hereafter called ‘Monograph 4’; SPEE 2016). This paper outlines a practical engineering workflow enabling companies to evaluate unconventional plays developed with horizontal multi-stage fractured wells consistent with the principles summarised in Monograph 4. This workflow has many applications including assessing potential acquisitions, defining new plays, evaluating competitor results, corporate budget processes, long-term business planning, portfolio management and reserves certification. The workflow, developed and refined over several years, has proven effective in large-scale applications. It enables engineers to readily identify and assess flow regimes, estimate the time to boundary dominated flow and estimate the flow patterns of boundary dominated flow for large groups of wells. The workflow also allows the engineer to deal with changing well designs and completion techniques. Throughout the workflow, the geological, engineering and statistical methods described in Monograph 4 are used. This provides the foundation to define and create representative type curves, yielding statistically reliable estimates of expected ultimate recovery (EUR) and production forecasts for asset evaluation with an accompanying characterisation of the confidence of these estimates. A case study demonstrating application of this workflow and a summary of results are presented. Potential sources of error in the technical analysis and application of type curves are identified; the technical and commercial impacts of these errors are highlighted. By allowing the evaluator to focus time and attention on the details of the technical analyses, companies can achieve a quicker, more in-depth analysis of the development of these large-scale unconventional resource projects.

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.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.205
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.055
GPT teacher head0.327
Teacher spread0.272 · 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