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Record W1989053043 · doi:10.4043/17685-ms

Casing Drilling Rig Selection Process For The Stratton Field

2005· article· en· W1989053043 on OpenAlexaff
D. Bickford, M. Mabile

Bibliographic record

VenueOffshore Technology Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsCasingDrillingProcess (computing)Selection (genetic algorithm)Computer sciencePetroleum engineeringField (mathematics)EngineeringGeologyEngineering drawingMechanical engineeringArtificial intelligenceOperating systemMathematics

Abstract

fetched live from OpenAlex

Abstract Apache Corp. was investigating new methods to efficiently produce trapped gas in its Stratton Field in South Texas. Recent wells drilled conventionally did not achieve the desired commercial and operational objectives. Casing drilling was selected as a method to achieve these objectives through the operational efficiencies it achieves1 and the ability to minimize lost circulation. Two options were considered during the rig selection phase of the project. The first option was to utilize a purpose built casing drilling rig and the second option was to convert a conventional rig to drill with casing. The project also dictated that a retrievable casing drilling system2 be used to meet logging requirements. Specialized equipment to drill with casing and to retrieve bottom hole assemblies was required on these rigs. Key components of the analysis included the commercial implications of the rig choice, operational performance, and rig crew experience with casing drilling. Introduction The Stratton Field discovered in the late 1930's3, is a gulf coast field located about 45 miles west of Corpus Christi. To date, approximately 495 wells have been drilled in the Stratton Field on a spacing of 20 to 40 acres. The previous drilling program conducted in 2000/2001 yielded below average results both commercially and operationally. The below average performance can be attributed to three main areas. The first area of concern was the amount of drilling fluid lost to the well bore during drilling and its effect on well production and well economics. The second was well bore stability through the Anahuac shale section. The third area was a low drilling rate of penetration in shale sections of the wells. A pilot project utilizing casing drilling was selected as a possible solution. Casing drilling was selected as a method to drill the wells with minimum losses and in turn less risk. The analysis also determined that the wells could be drilled in less time minimizing time dependent well bore stability problems in the Anahuac shale. Casing drilling was also selected based on the analysis that the wells could be drilled more efficiently and with equal or greater rates of penetration than with conventional drilling. A three well program was conducted to test the casing drilling technology and its ability to lower costs, minimize lost circulation, and lower drilling risks. Due to certain requirements of the drilling and completion program, a retrievable casing drilling system was selected. This required the use of a rig capable of using a retrievable casing drilling system. The rig selection centered on two options; utilization of a rig purpose built for casing drilling or a conventional rig converted to handle casing drilling with retrievable systems. This paper explores the rig selection process and its effect on the drilling program. Required Equipment for Casing Drilling Critical to the analysis were the actual additional rig components required to drill with casing. The additional components included a top drive, casing drive assembly, split crown block, split traveling block, wireline winch, and wireline blowout preventers.

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 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 score0.861
Threshold uncertainty score0.511

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 integrity0.0000.000
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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.

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

Citations2
Published2005
Admission routes1
Has abstractyes

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