Failure to Produce: An Investigation of Deficiencies in Production Attainment
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.
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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 | 0.027 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".