Environmentally Friendly Fracturing Fluid as Utilized in a Western Canada Sedimentary Basin Case Study
Bibliographic record
Abstract
Abstract In recent years, the oil and gas industry has been highly focused on reducing the environmental footprint caused by hydraulic fracturing fluids. This parallels society's fixation on developing "greener" technologies and processes. Environmentally friendly or "green" is a vague term referring to processes and products that reduce or eliminate any impact to human health and the environment (US EPA, 2014). Although this is a common goal among those in the oil and gas industry, there is a lack of structure around what defines "green." The definition varies across continents, governing bodies, companies, departments, and individuals, with each employing a different set of criteria they deem relevant. Developing a process for environmentally responsible fracturing without a clear and accepted definition is a cumbersome undertaking. This paper describes a process by which the concept of safer and "greener" hydraulic fracturing operation can be accomplished, with a strong focus on the chemical components of the fracturing fluid. The discussion will center around: health and safety of personnel, environmental impact of utilized chemistries, geographical applicability in conjunction with corresponding local regulatory policies, and an in-house developed chemical hazard rating system. The application of this process will be described through a case study which includes more than 100 pumped intervals in multiple geographic areas. The case study provides: stimulation job designs, treatment reports, and production results. Once a specific set of criteria has been established, a system that meets the aforementioned criteria needs to be developed. This process entails evaluation of each system component for environmental applicability and system performance. Additionally, through collaboration with chemical manufacturers, toxicologists, and fluid experts, the technical accuracy and applicability of toxicological data can be determined. A cradle-to-grave development of a foamed linear fluid system will be the example illustrated in this paper to showcase the development and implementation of a "green" hydraulic fracturing fluid system. This specific system encompasses a significantly reduced environmental profile in comparison to similar systems, while maintaining equivalent performance and overall cost to customer. The fluid system has been optimized and continues to be pumped with great success.
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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".