Fingerprinting Stray Formation-Fluids Associated With Hydrocarbon Exploration and Production
Bibliographic record
Abstract
In the past few years there has been an increase in exploration and production (E&P) activities using hydraulic fracture treatments to exploit both conventional and unconventional oil and gas resources. Public scrutiny over potential environmental and/or health impacts arising from unwanted migration of stray fluids (oil, natural gas, brine, flowback fluids) associated with these activities has lead to some jurisdictions banning E&P activities outright. Thus understanding the source (or fingerprinting) of stray fluids is more important to the E&P industry than ever before. Fortunately there are many time-tested geochemical methods, along with new scientific advances, that are successfully being applied to the issue of stray fluid migration. Standard fingerprinting techniques have historically been based upon molecular chemistry and “type” diagrams for formation fluids and natural gases. Using a standard fingerprinting method involves comparing the chemical pattern of a stray fluid sample against a series of type-fluid diagrams to determine the origin of the stray fluid. These standard methods work well, but not always: there is the possibility of an unknown type diagram for that area/formation; and there is always the chance that the stray fluid chemistry is not unique to one or more of the type diagrams. Stable isotope compositions of formation-waters and gases are proving very useful for determining the source of stray fluids. For example, carbon isotopes have been used for decades to fingerprint the source of Surface-Casing-Vent-Flows in conventional oil and gas wells, and more recently for unconventional gas wells in the Barnett and Fayettville plays in North America. Noble gas isotopes are now being applied to stray gas investigations in many areas in eastern United States. Stable isotope techniques are also able to fingerprint the origin of stray formation-fluids when standard molecular composition-based methods fail. This talk will provide new examples of the use of isotope-based fingerprinting techniques in hydrocarbon E&P operations, including: characterizing the source of flowback fluids after hydraulic fracture treatments; identifying stray fluids from Surface-Casing-Vent-Flow from oil and gas wells; and identifying the source of produced-water spills at the surface. These isotopic techniques hold great promise for increasing safety, reducing public health concerns, and improving the economics of hydrocarbon wells.
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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 | 0.002 |
| 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".