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Record W4415777312 · doi:10.2118/229499-ms

Well-Design Optimization Enables Drilling Performance Improvement in Unconventional Shale Gas Project, Canada

2025· article· W4415777312 on OpenAlexaffabout
Shupin Zhang, Longlian Cui, Min Yang, Xiangguo Zhao, Liangyu Rao, Chunyang Hong

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsBP (Canada)
Fundersnot available
KeywordsDrillingDirectional drillingMeasurement while drillingCompletion (oil and gas wells)Rate of penetrationDrill pipeOil shaleMud loggingBorehole

Abstract

fetched live from OpenAlex

Abstract In North America shale gas projects, operators aim to extend the horizontal sections to boost single well production and reduce drilling and completion costs. However, as the horizontal section length increases, especially when the laterals exceed 3500 meters, numerous complex drilling challenges arise. These include issues with weight transfer during slide drilling, high downhole friction and torque, mud losses caused by high mud weight and friction loss in long horizontal section, short service lives and low efficiency of downhole tools, limited capability to track thin strata due to restricted logging equipment, and difficulties in deploying production casing, including risks such as differential sticking and prolonged running times. To address these challenges, this paper presents an optimized well design strategy for the drilling of unconventional extended-reach unconventional horizontal wells. Firstly, a slim hole design is adopted to cut costs related to casing, cement, and drilling fluids. Secondly, the upper drilling string is switched from 4″ to 4.5″ drill pipes to provide enough torque and hydraulic power, and the Bottom Hole Assembly (BHA) utilizes even-wall stator positive displacement motors (PDM) and motorized rotary steerable systems (RSS) to enhance drilling efficiency in the intermediate and horizontal sections. Third, the oil-based mud (OBM) system is optimized for stability, low friction, and employs clay-free invert emulsion, complemented by managed pressure drilling (MPD) to control ECD. Then, downhole vibration sensors integrated into RSS enable predictive maintenance against tool fatigue. Next, drilling bit optimization based on formation strength analysis increases rate of penetration and extends bit life. Next, dual-telemetry xBolt (MWD) technology, combined with azimuthal gamma ray logging (LWD), which precisely reveals bed crossings and boundaries, optimizes wellbore placement. Finally, a specialized casing running tool and robust casing connection ensure successful deployment of 5.5-inchcasings in long laterals. These optimizations delivered outstanding outcomes. Several records were broken, such as the longest 4,875-meter horizontal slim-hole section drilled in a single bit run, with the average sweet zone drilling ratio of 96%, the longest single-run deployment of 5.5-inch and 4.5-inch hybrid casing at a depth of 8,157-meter, and a rapid 17-day drilling cycle without incidents. In recent years, numerous horizontal wells have extended beyond 4,000m in lateral length, achieving an average horizontal rate of penetration (ROP) exceeding 50m/h and an average drilling duration of within 20 days. Finally, These drilling optimizations deployed in the Duvernay shale project have notably reduced per-meter costs and enhanced drilling speed, setting a new benchmark within the Western Canadian unconventional project. These practices has set the stage for continued drilling of extended reach horizontal wells in the development of similar unconventional reservoirs.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.185
Teacher spread0.178 · 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 designNot applicable
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 routes2
Has abstractyes

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