Bibliographic record
Abstract
The energy transition is in a continuous pursuit of innovative technology applications from all corners of the oil and gas industry. With the exponential growth of carbon capture and sequestration (CCS) projects, similar subsurface appraisal objectives remain. Derisking dynamic reservoir performance and characterizing storage pore space are key enablers for prospective CCS projects. The mention of well testing often takes readers to a visualization of hydrocarbon disposition by a flare. Once a bright and vibrant spectacle of our industry’s exploration updates, the days of flaunting a chairman’s flow or banker’s burn are past. Waning application of well testing during our energy transition is a specious assumption, and it’s the optics that are transforming as the well testing discipline proves its value to long-term carbon storage projects. Injecting CO2 into the subsurface is hardly a new concept; operators have been doing this for decades as part of enhanced oil recovery. However, the application of injecting CO2 is changing in response to operators’ environmental, social, and governance ambitions. While carbon storage concepts exist in many forms, anchoring a storage project requires pore space of adequate quantity and quality in proximity to a fixed source of emissions. Carbon storage projects also must manage additional risks such as sustained injectivity performance, geologic seal integrity, and plume migration, to name a few. Enter well testing. Quantifying injection performance of supercritical fluids is crucial for optimizing wells to meet project needs while minimizing the number of required penetrations through a structural seal. Whether for a saline aquifer or a previously depleted field, pressure transient analysis enhances understanding of the pore space intended for storage and the potential heterogeneities within. Furthermore, integrating well testing with other reservoir-description tools may be used to monitor migration of stored fluids within the reservoir. With corporate and regulatory targets driving the unprecedented pace and scale of CCS opportunities, well testing is quickly reaffirming itself as a powerful tool in characterizing pore space essential to our lower-carbon goals. This month’s papers highlight ongoing developments within the well testing discipline and important reminders about how to properly use dynamic data. The application of these well testing fundamentals to a nascent carbon storage market is still evolving. Recommended additional reading at OnePetro: www.onepetro.org. SPE 208967 Rate-Pseudopressure Deconvolution Enhances Rate-Time Models Production History-Matches and Forecasts of Shale Gas Wells by L.M. Ruiz Maraggi, The University of Texas at Austin, et al. URTeC 3705570 Analysis of Multiple Flow/Buildup Tests Including a 5-year Buildup: Case Study of an Australian Shale Gas Well by Christopher R. Clarkson, University of Calgary, et al. OTC 31691 Challenges and Mitigation Strategies for High-Rate Gas Well Testing in High-Pressure/High-Temperature DST Operation by Jakpakorn Hemaprasertsuk, PTTEP, et al.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.458 | 0.336 |
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 source (direct Gemma or distilled Codex), 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".