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Record W2395660182 · doi:10.2118/180745-ms

Technical Benchmarking: A Critical Step in Reducing Costs in a Low Price Environment

2016· article· en· W2395660182 on OpenAlexaboutno aff
George C. Brindle, Chantel M. Moran, Paul Goolcharan, Jason Perry

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2016
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingScope (computer science)Consistency (knowledge bases)Computer scienceSizingMetric (unit)Identification (biology)Key (lock)Control (management)Measure (data warehouse)Engineering design processRisk analysis (engineering)Construction engineeringSystems engineeringIndustrial engineeringManufacturing engineeringReliability engineeringEngineeringOperations managementBusinessMechanical engineeringDatabaseMarketing

Abstract

fetched live from OpenAlex

Abstract Objectives/Scope Expertise has brought consistency and cost control to drilling and completing steam assisted gravity drainage (SAGD) well pairs. Data shows that the well pad facilities have not achieved the same degree of consistency in scope. Broad differences in specifications, physical design and execution have existed and continue to exist. We intend to show the benefits of technical benchmarking for scope and specification control in the design and execution of SAGD well pads. Methods, Procedures, Process Our approach is to measure constructed and operating well pad designs with 511 category measures which rollup into 42 key design metric categories. We believe that the operating well pads provide evidence of functionality and show good engineering practice. Our interest is in showing the minimum of each kind of equipment, pipe and instrument building block that is actually required to provide a functioning site. Results, Observations, Conclusions Our data comes to us under confidential contract from many of the SAGD producing companies and engineering firms, either for estimating or for comparative analysis of designs with industry normal prior to sanction. We reviewed well pad design elements, physical measurements, sizes and counts and compared them across both industry and design to reveal opportunities for optimization. Our repeated studies of the operational and regulatory compliant well pad scope show that some projects used many times more items than other projects. Large differences in well pad dimensions were often combined with large differences in equipment sizing. Large differences in bulk materials were also observed. Through the identification of design limitations, a number of project teams incorporated significant reductions in on-pad dimensions, pipe and equipment sizes and reductions in counts of instrumentation hardware. This process leads to simplification of the design, allowing for a reduction in capital expenditure (CapEx) and operating costs (OpEx) while ensuring it is still safe and easy to operate. In a low oil-price environment, it is essential to produce well pads comprised of design elements and execution that have measurable success in all areas. Project managers and executives alike need to know that the design teams are utilizing the minimum practical combination of sizing, redundancies and tonnages of materials in execution. It is our belief that good engineering will show what to include but great engineering shows what can be left out. We propose that a minimum, safe, functional scope when combined with good contract strategy will bring the thermal producers supplemental well pad costs that will meet capital requirements in our new low Western Canadian Select crude oil price environment. NOTE: At no point will the contracting entities or producers be identified in this paper. Novel/Additive Information We have seen no other recent work that uses this approach to control scope or specification. We believe the technique is universally applicable to any unconventional development such as shale gas, SAGD or similar.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.222
Teacher spread0.212 · 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 teacher head, not a consensus.

Study designOther design
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
Published2016
Admission routes1
Has abstractyes

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