Developing a fuzzy logic-based risk assessment for groundwater contamination from well integrity failure during hydraulic fracturing
Bibliographic record
Abstract
Recent natural gas development by means of hydraulic fracturing requires a detailed risk analysis to eliminate or mitigate damage to the natural environment. Such geo-energy related subsurface activities involve complex engineering processes and uncertain data, making comprehensive, quantitative risk assessments a challenge to develop. This research seeks to develop a risk framework utilising data for quantitative numerical analysis and expert knowledge for qualitative analysis in the form of fuzzy logic, focusing on hydraulically fractured wells during the well stimulation stage applied to scenarios in the UK and Canada. New fault trees are developed for assessing cement failure in the vertical and horizontal directions, resulting in probabilities of failure of 3.42% and 0.84%, respectively. An overall probability of migration to groundwater during the well injection stage was determined as 0.0006%, compared with a Canadian case study which considered 0.13% of wells failed during any stage of the wells life cycle. It incorporates various data types to represent the complexity of hydraulic fracturing, encouraging a more complete and accurate analysis of risk failures which engineers can directly apply to old and new hydraulic fracturing sites without the necessity for extensive historic and probabilistic data. This framework can be extended to assess risk across all stages of well development, which would lead to a gap in the modelled and actual probabilities narrowing. The framework developed has relevance to other geo-energy related subsurface activities such as CO2 sequestration, geothermal, and waste fluid injection disposal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".