Bibliographic record
Abstract
R&D Grand Challenges - This is the fifth in a series of articles on the great challenges facing the oil and gas industry as outlined by the SPE Research and Development (R&D) Committee. The R&D challenges comprise broad upstream business needs: increasing recovery factors, in-situ molecular manipulation, carbon capture and sequestration, produced water management, higher resolution subsurface imaging of hydrocarbons, and the environment. The articles in this series examine each of these challenges in depth. The R&D Grand Challenges Series, comprising articles published in JPT during 2011 and 2012, is available as a collection on OnePetro (SPE-163061-JPT). Introduction It is hard to read road signs if you have poor eyesight, which is why driver’s licenses are issued with restrictions requiring that corrective lenses must be worn. Likewise, it is hard to find and exploit subsurface resources if you can’t clearly see your targets or monitor the movement of fluids in the reservoir. Engineers now have powerful tools to precisely model subsurface reservoir production behavior, but a precise answer is still wrong if it is derived from an inaccurate subsurface description. Geoscientists make maps and rock property models of the subsurface by interpreting images that are produced from remote sensing data. Analogs from modern depositional environments and outcrop exposures guide subsurface data interpretation to predict ahead of the bit, then postdrill geostatistics are used to fill in stratigraphic details between wellbore control points. Selection of the right depositional model, facies distribution, and geostatistical analog depends on having the sharpest, most detailed and accurate image of the subsurface possible—the Grand Challenge of Higher Resolution Subsurface Imaging. Over the past century, the industry has relentlessly sought ways to improve subsurface imaging of hydrocarbons. Canadian inventor Reginald Fessenden first patented the use of the seismic method to infer geology in 1917. A decade later, Schlumberger lowered an electric tool down a borehole in France to record the first well log. Today, advances in seismic and gravity data acquisition, electromagnetics, signal processing and modeling powered by high-performance computing, and the nanotechnology revolution are at the forefront of improved reservoir imaging. In this paper, we will examine the challenges of getting higher resolution subsurface images of hydrocarbons and touch on emerging research trends and technologies aimed at delivering a more accurate reservoir picture.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.019 |
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".