MétaCan
Menu
Back to cohort
Record W2010931439 · doi:10.2118/160655-ms

Evaluating the Impact of Fracture Proppant Tonnage on Well Performances in Eagle Ford Play Using the Data of Last 3-4 Years

2012· article· en· W2010931439 on OpenAlexaff
Chao Gao, Changan Du

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsDevon Energy (Canada)
Fundersnot available
KeywordsTonnageEagleHydraulic fracturingPetroleum engineeringGeologyProduction (economics)Stage (stratigraphy)Fracture (geology)Mining engineeringDrillingEngineeringGeotechnical engineeringPaleontologyMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Since 2008, horizontal wells completed with multi-stage hydraulic fracturing have dramatically boosted Eagle Ford production. Due to high drilling and completion costs, operators have been trying to improve development economics by optimizing the stimulation treatments. This paper presents a look-back analysis on the stimulation treatments performed by different Eagle Ford operators from 2008-2012 using data mining methodologies, focusing on how proppant tonnage has impacted early-time production of their Eagle Ford wells. The Eagle Ford wells have been observed having high decline rates and the early time production performance evaluation will be critical in project economics evaluations and EUR forecasts. The results of this look-back analysis may be used for field development optimization, where to drill and how much proppant should be pumped in each of different areas of Eagle Ford play. The focus of this paper is to present how proppant tonnage could impact the early time production performance. However, there are many variables affecting the early time performance of Eagle Ford wells. In this paper, we present data using bubble maps, contour maps, grid maps and scatter plots to show how some major geological and reservoir engineering variables could affect the early time performance of the Eagle Ford wells. The wells with similarities are grouped in order to minimize the variation of geological variables and other engineering variables and to derive consistent correlations between proppant tonnage and early time performances in different well groups. We present data in different well groups to show the correlations between proppant tonnage and early time well performance. For this study, the authors built a database of 3000+ Eagle Ford wells (including 2300+ horizontals). The authors found that it was very challenging to clean the public data and reformat it to a presentable manner.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.073
GPT teacher head0.359
Teacher spread0.286 · 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 designObservational
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

Citations8
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueSPE Annual Technical Conference and ExhibitionSame topicHydraulic Fracturing and Reservoir AnalysisFrench-language works237,207