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Record W3215174492 · doi:10.2118/208481-ms

Optimising Depth Selection on Section Milling Operation to Secure Sustained Casing Pressure During Plug and Abandonment Operation

2021· article· en· W3215174492 on OpenAlexaff
Dianita Wangsamulia, Khresno Pahlevi, Simon Paulus, Gama Hafizh Aditya, Heri Tanjung, Ratna Dewanda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsCasingSpark plugBoreholeAnnulus (botany)PetrophysicsPetroleum engineeringDrillingPerforationGeologyPorosityMechanical engineeringEngineeringMaterials scienceGeotechnical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract D-01 was an exploration well requiring a Plug-and-Abandonment (P&A) procedure with sustained casing pressure up to 2,000 psi on the B annulus. The presence of Sustained Casing Pressure (SCP) is one of the major technical challenges to decommission and abandon the well safely. Several attempts to secure the well using the perforation-and-squeeze method were performed – but failed. It was decided to perform section milling operations to create a viable rock-to-rock barrier. In this operation, the key factor in determining success, is selecting the correct depth to mill safely and secure the annular pressure source. A comprehensive approach was taken to determine the optimum depth for the section milling by evaluating existing open-hole and cased-hole data. Additionally, triple-detector Pulsed Neutron Log (PNL) was also performed prior to the section milling operation. The triple-detector PNL tool offered not only behind casing porosity (TPHI) and sigma (SIGM) measurement, but also a relatively new measurement in the oil and gas industry called Fast Neutron Cross Section (FNXS), which were expected to provide more accurate gas detection and gauge the condition near the borehole. By combining all the logs and reservoir data, the milling interval was selected and the section milling and subsequent cement plug operations were performed. Evaluation of existing open-hole and cased-hole logs provided geological and petrophysical insights which were useful in determining the hydrocarbon source charging the B-annulus. Further analysis on PNL data provided indication of possible gas pockets in the B-annulus. This information was used to distinguish between shallower formation sources or gas pockets that were not yet bled off. The operation on D-01 successfully resolved the B-annulus issue and the well was properly abandoned per regulatory requirements. Considering the complexity and high cost of section milling operations, a thorough review of data and pre-job logging increases the probability of selecting the optimum intervals needed to successfully complete P&A operations on SCP wellbores.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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