Bibliographic record
Abstract
Abstract During the last three decades, Risk Analysis has become an important tool for the Oil Industry. Risk Analysis methods have been used in a variety of ways as a means to evaluate exploration and development projects as well as a tool for investment decision. In addition, use of Risk Analysis methods for economic and engineering applications has become widely accepted. This article presents a review of the use of this tool specifically for drilling operations and describes the development and application of a method for risk analysis in fishing operations. The decision-making method described, that can be applied for various oilwell operations, is based on risk analysis theory and uses previous operation results in order to analyze if the operation being carried out is the one with the highest probability to conduct to the best economical result. An example of the method application utilizing actual field data is provided. Introduction Risk analysis can be a useful tool on certain processes where uncertainty is involved. In the last 35 years a number of articles have described methods where the use of risk analysis was deemed as fundamental to minimize losses or to maximize the possibility of adopting, for a certain situation, the right decision. Particularly, for drilling operations there have been various articles where different aspects of the drilling process have been studied using risk analysis as an auxiliary tool on the decision-making process. In 1968 a fundamental article1 was published relating risk analysis and drilling investment decision. This article did not deal specifically with any particular drilling process or operation; instead, it presented a method for assessing the degree of uncertainty involved in investments for exploratory drilling. Even though not dealing directly with drilling operations, that article presented a method to handle uncertainties that clearly could be extrapolated for dilemmas faced regularly on ordinary well operations. After that, various authors investigated the possibility of using risk analysis not only as a tool to be applied on resolutions regarding drilling exploratory prospects2, but also for specific drilling operation decisions like optimum depth to set a casing3, directional drilling4, wireline operations5, special remedial operations6, borehole stability7 and prediction of pore pressure and fracture gradient8. Also articles were written relating the use of risk analysis with safety and reliability of drilling operations9 as well as prediction of overall drilling costs10. Despite the large availability of theoretical sources and computational tools, use of risk analysis on drilling operations remains limited mainly due to the fact that it is still considered a sophisticated and complex tool which implementation is extremely intricate. Besides that, use of risk analysis tools requires methodical quantification of uncertainties and use of data from past operations that not always are easily available. This article presents a simple method to implement risk analysis on drilling operations requiring decisions under uncertainty. An example of application is also presented. Using Risk Analysis Implementation of risk analysis involves three basic steps: identifying an opportunity (or event) where the tool can be applied, quantifying the consequences of various possible decisions and assessing, within the possible outcomes, the estimated best economic or operational result.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".