Bibliographic record
Abstract
Special Section: The Value and Future of Petroleum Engineering The need to understand the future trends of the oil industry has never been greater than it is today. Throughout the history of the oil industry, technology and innovation have made a significant contribution by pushing the boundaries to enable a continuous expansion in production, increasing reserves, and capital efficiency. In the years to come, with the world’s conventional reserves declining, energy companies will inevitably have to move into more challenging and remote locations to explore and produce hydrocarbons. Therefore, the role of innovation and, more specifically, data-science-derived technologies will likely become the key to shaping the future of the oil and gas sector. In fact, there are ample opportunities for oil and gas companies to use Big Data to get more oil and gas out of hydrocarbon reservoirs, reduce capital and operational expenses, increase the speed and accuracy of investment decisions, and improve health and safety while mitigating environmental risks. It is worth mentioning that although the so-called “disruption mandate” faced in every industry, including oil and gas, is not new, its current speed and complexity will foster an adaptive mindset while maintaining its business practices—the key not only for success but also for survival in the age of the digital transformation. Technological advances, such as increased use of Web-based platforms and cutting-edge data-acquisition technologies such as sensors, have made it possible to generate a staggering amount of data in the industry—the aforementioned Big Data—often which is not used efficiently or effectively. One of the key enablers of the data-science-driven technologies for the industry is its ability to convert Big Data into “smart” data. New technologies such as deep learning, cognitive computing, and augmented and virtual reality in general provide a set of tools and techniques to integrate various types of data, quantify uncertainties, identify hidden patterns, and extract useful information. This information is used to predict future trends, foresee behaviors, and answer questions which are often difficult or even impossible to answer through conventional models. Automation, which is derived from Big Data analytics, is a huge step in the direction of improving data science in the immediate future. This evolution holds added benefits such as improving operational efficiency, reducing operational costs, increasing speed, and enhancing self-service modules. The need to automate business processes with the goal of improving functionality and increasing efficiency will be the main driver for the increased adoption of data sciences in the industry. A significant potential for automation exists because it can serve as an ideal aid to daily operations. Many areas where automation can make an immediate and lasting difference for the oil and gas sector include identifying new well targets, improving drilling efficiency, optimizing artificial-lift systems, and monitoring onshore and offshore pipelines and other relevant facilities.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".