Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".