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Record W2916402494 · doi:10.2118/0219-0053-jpt

Technology Focus: Well Testing (February 2019)

2019· article· en· W2916402494 on OpenAlexaboutno aff
Heejae Lee

Bibliographic record

VenueJournal of Petroleum Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)Computer scienceKey (lock)Simple (philosophy)Focus (optics)Risk analysis (engineering)Set (abstract data type)Computer securityOperations researchEngineering

Abstract

fetched live from OpenAlex

Technology Focus Years ago, when I asked my mentor what the key to a successful well test was, he said, “Clear objectives, the right equipment, attentive operations, and comprehensive analysis.” I joked that it sounded quite simple and obvious, to which he responded, “Simple and obvious doesn’t mean easy to achieve.” So, here we go, years later, with my contemplation of the simple and the obvious of a successful well test. Clear Objectives First, establish clear and specific objectives using a systematic approach and align them with all stakeholders. From design and implementation to data collection and analysis, test objectives should remain the go-to framework for decision-making. The Right Equipment Advances in equipment enable us to gather data beyond the capabilities of what was previously feasible: high-resolution gauges, wireless telemetry, distributed temperature sensing, real-time flow-control devices, advanced bottomhole and surface sampling techniques, and multiphase flowmeters, to name but a few. We understand that hardware dictates the quality of the data. We should also consider equipment fit for purpose with value of information in mind while remaining committed to safety and tolerant of uncertainty. A deepwater exploration test will require a different set of equipment than a diagnostic fracture injection test (DFIT) or a production-allocation test. Attentive Operations Procedures should be in place to achieve success, such as basis of design, risk assessment, well tests on paper, detailed operation procedures, and meetings (e.g., prejob safety, pretour). But, as Murphy’s Law tells us, what can go wrong will go wrong: Equipment may fail, people may make mistakes. Complacency is the enemy, so operational personnel should always remain alert and keep uncertainty and contingency in mind. As always, safety is the No. 1 objective and the most critical consideration. Comprehensive Analysis Analytical capabilities have progressed since the days of the semilog plot, with, for example, various type curves and near-wellbore/boundary models, carbonate and fracture models, deconvolution, non-linear modeling, interference tests, horizontal wells, and DFITs. While the new digital era will provide insights from machine learning and automation from massive amounts of information, foundational data still should be collected and quality checked. Subsurface remains inherently a nonunique problem to solve, so, rather than mindlessly fitting the data, the engineer still will need to consider what makes sense with uncertainty in mind. The papers selected for this issue focus on key factors in achieving a successful well test. They also apply reservoir fundamentals as well as sound engineering judgment, with examples from conventional and unconventional assets. Recommended additional reading at OnePetro: www.onepetro.org. SPE 189826 DFIT Analysis in Low-Leakoff Formations: A Duvernay Case Study by Behnam Zanganeh, University of Calgary, et al. SPE 189840 Reinterpretation of Flow Patterns During DFITs on the Basis of Dynamic Fracture Geometry, Leakoff, and Afterflow by Behnam Zanganeh, University of Calgary, et al. SPE 189844 Estimating Unpropped-Fracture Conductivity and Compliance From Diagnostic Fracture Injection Tests by Han Yi Wang, The University of Texas at Austin, et al.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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