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Record W2783509403 · doi:10.2118/189423-ms

New Approach: Confirming Well Quality at Well Delivery with Stringent Well Integrity Checks at a World Class Drilling Project

2018· article· en· W2783509403 on OpenAlexaff
Stephen Butt

Bibliographic record

VenueSPE/IADC Middle East Drilling Technology Conference and Exhibition · 2018
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPerformance indicatorQuality (philosophy)Computer scienceScale (ratio)Set (abstract data type)DrillingEngineeringBusiness

Abstract

fetched live from OpenAlex

Abstract Drilling a well is comprised of multiple activities which are linked to the well objectives and requirements set in the design phase. Some of the activities have short term impacts on the well such as logging a section etc., and some of the activities have long term impacts on the well such as cementing, wellbore accessibility etc. It is quite important to list the activities based on their impact on a well and rate them individually to get the overall impact on the objectives of a well by these activities. Conventionally a well quality score was reported 6-12 months after a well was completed. The quality cycle to improve the performance of a well became ineffective and irrelevant due to late reporting. The results of the activities of a completed well were so late that many wells had been drilled and completed during the reported period. First, this major flow turned the existing Well Quality KPIs into laggard KPIs, which were not contributing to enhancing the Quality of a delivered well. Second, the well quality score was distributed among four different categories where Well Integrity was an isolated category, and a well integrity issue has minimum impact on overall well quality scoring. Third, the scoring guidelines were very generic and were depended on the evaluator judgment. A lack of verification of the results was also evident during KPI reporting, which made the KPIs score skeptical and unreliable. Fourth a fixed scoring structure was used to evaluate all type of wells at the same scale. Such as the scoring of a complex well was treated the same manner as a scoring on a workover well. Last, some activities were ignored in the well quality scoring such as Coring Quality, minimum Well Integrity requirements etc. The overall score does not represent the actual picture of a well using existing Well Quality KPIs, which was impacting the overall project quality score. A new approach was adapted to capture the well quality score right after a well is delivered so that improvement ideas can be implemented in the current drilling wells in the execution phase and coming wells in the design phase without any delays. The quality cycle was improved resulting in shorter well duration with lesser well integrity issues. A new weightage system was introduced to capture all activities in a well, where these activities are evaluated individually. Scoring criteria for each activity is defined clearly. Based on deviation from the planned activity, the actual score is recorded accordingly by the user. Later these activities are verified by the end users, so verification is enhancing the trust as well the validity of a lesson learned. Users and end users are connected at an early stage after a well completed to capture the feedback. Improvements get quickly implemented as the quality cycle is short and quick. The new scoring method introduced a wide range of Well Integrity checks based on rigorous and clear guidelines, where failure to meet key well integrity policies can result in nulling the overall score of a well. New well quality scoring guidelines provide a clear and efficient approach to score the key performance indicators of a well at the right time. Consistency in scoring, timely reporting and right weightage for well quality scoring results in high quality well programs, application of fit-for purpose technologies and better knowledge transfer among team members.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.051
GPT teacher head0.239
Teacher spread0.188 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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