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Well spacing optimization technology for coalbed methane horizontal wells

2025· article· en· W7084046922 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsCoalbed methaneChartMaximizationRationalization (economics)Structural basinFlow chartPressure dropProduction (economics)Coal mining

Abstract

fetched live from OpenAlex

BackgroundWell spacing design directly affects the resource utilization and exploitation benefits of blocks. Rational well spacing design plays a significant role in the commercial exploitation of coalbed methane (CBM) fields. Since the beginning of China’s 14th Five-Year Plan, blocks for CBM production in the southern Qinshui Basin have experienced a gradual increase in burial depth, a progressive decline in permeability, and a constant update of primary exploitation technology. Consequently, traditional well spacings fail to meet the requirements for field CBM exploitation any longer. MethodsThis study investigated the western Qinnan – eastern Mabi block by combining numerical simulations with economic assessments. Using the geology-engineering economy integration approach and based on the mechanisms underlying pressure drop propagation and synergistic desorption, this study analyzed the factors influencing well spacings. Moreover, a flow chart for determining optimal CBM well spacings was established. Accordingly, the optimal well spacings under given economic parameters and varying combinations of geological conditions and engineering parameters were determined. Results and ConclusionThe results indicate that important factors influencing the well spacing include permeability, gas content, and fracture half-length, which exhibit positive correlations with the well spacing. For the CBM well spacing design, it is necessary to predict the production capacity and recovery degree under varying combinations of geological and engineering parameters using numerical simulation. The optimal well spacings should be calculated based on the calculation of economic limit well spacings, as well as the maximization of single-well estimated ultimate recovery (EUR), the rationalization of recovery degrees, and economic benefits. The chart board of optimal well spacings was established based on the actual geological, engineering, and economic parameters of the western Qinnan – eastern Mabi block, guiding field production practices and providing a reliable basis for the well spacing optimization design in the production capacity construction of the block. Conducting rational well spacing optimization using the geology-engineering economy integration approach by considering pressure drop propagation and synergistic desorption will be a significant trend in CBM production. The results of this study provide a valuable reference for the rational and efficient exploitation of CBM 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 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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.076
GPT teacher head0.497
Teacher spread0.422 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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