Bibliographic record
Abstract
Guest Editorial The oil and gas industry has an amazing opportunity at present; the demand for energy around the globe is increasing and our industry has answered the call in the form of unconventional oil and gas production. Advancements in directional drilling and hydraulic fracturing have transformed low-quality resources into economically viable sources of energy. Over the past 7 years, exploration and production (E&P) companies have increased production rates, but not necessarily recovery rates, and therein lies the challenge. Predicting and maximizing estimated ultimate recovery (EUR) is crucial for moving from efficient to effective unconventional shale completion operations. E&P companies are drilling and producing shale reservoirs at an increasing rate, which is contributing significantly to North American oil and natural gas; however, significant reserves are being left behind. Techniques for predicting EUR vary by operator. Some calculate EUR by extrapolating from initial production rates while others may apply decline curve trends from one play to another. Each method has its advantages and each its caveats. Operators need 6 months to 1 year of production history to truly estimate EUR. However, extrapolation of initial production rates rarely tells the whole story. Some operators may have insufficient data to understand the longer-term behavior, and though traditional decline curve analysis has proven effective for prediction of conventional reservoir production, we are seeking different, more realistic methods to apply to unconventional shale reservoirs. The variability in unconventional shale plays is leading to unpredictable performance from wells. Therefore, as an industry, we must realize that understanding the composition and behavior of the target geology during fracturing is essential to hit the “sweet spots” and ultimately will result in maximizing the amount of recoverable reserves. One of the most important concepts that needs to be fully appreciated is that every well faces a unique set of circumstances. Well and stage spacing is at the forefront of maximizing the production and economics of each completion. We need to evaluate each distinct well to understand the relationship between EUR and well spacing, stage spacing, lateral lengths, and orientation to better optimize completions for each well. Services are needed for the recommendation of fracture treatment designs that maximize treatment efficiency, and a clear demonstration of the effect of completion designs on reservoir recovery. For instance, a technique that could predict initial production and EUR would enable operators to understand the economics of each well earlier to improve booked reserves. Today, approximately 62% of all wells fractured in the United States are horizontal wells. In an effort to reduce costs, E&P companies are “factory mode” drilling these wells, which means they are using identical well spacing, orientation, and fracturing techniques for every well. It is a one-size-fits-all approach that may keep costs low by shaving days off of the drilling process, but is failing to adequately deplete the reservoir.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".