Failure to Produce: An Investigation of Deficiencies in Production Attainment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The economic importance of delivering on planned production volumes is undeniable. Over the last 15 years, however, the oil and gas industry's production attainment performance has degraded. Today, the average oil and gas project delivers only 75 barrels for every 100 barrels promised at sanction. This paper reports on a root-cause analysis conducted on over 145 oil and gas projects for which the authors have access to planned production volumes (at sanction), and 12 months to 60 months of actual production data. The authors use a detailed global database of oil and gas projects to conduct a rigorous statistical analysis of production attainment. The analytical strategy is to statistically connect "inputs" (i.e., information and practices used prior to sanction) to "outputs." The results show that poor production attainment is due to unreliable forecasts based on optimistic subsurface assumptions, failure of assurance processes, and lack of accountability for production volumes. Our analysis shows that project teams are overly optimistic about basic subsurface characteristics, especially in the absence of actual data. Assurance and decision analysis processes, such as peer reviews and risks modeling, are not successful in identifying optimistic forecasts. Every project with a significant production shortfall used these tools, yet these tools failed to flag the risks. Most companies lack a single point of accountability for delivering production. In most cases, no one is accountable if the production falls short of promise. These problems persist because companies do a poor job of conducting root-cause analysis to understand production shortfalls; only 30 percent of projects in this database conducted such an analysis. The analysis provides strong evidence that the industry has a problem in predicting production volumes. But the authors go beyond this observation and provide the reader with valuable take-aways, including specific causes of the problem and recommendations to eliminate, or reduce, this problem.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it