INVESTIGATING CROSS FORMATIONAL FLOW OF FLUIDS THROUGH OIL AND GAS WELLS IN ALBERTA AND SASKATCHEWAN, CANADA
Bibliographic record
Abstract
Deep fluids that are buoyant or have been pressurized due to hydraulic fracturing (HF) or injection related to enhanced oil recovery (EOR) and salt water disposal (SWD) can migrate upwards into other aquifer units, including potable aquifers, through a number of pathways. However, operational considerations typically limit the likelihood of cross formational flow occurring through natural geologic pathways such as faults, leaving anthropogenic pathways, namely wellbores, as the most likely pathway for cross formational flow in most settings. This thesis first evaluates the potential for inter-borehole flow to occur through old abandoned wells in the deep subsurface associated with nearby HF and EOR or SWD operations at three study sites in Alberta and Saskatchewan. It then predicts the occurrence of surface casing vent flow and gas migration (the surface expression of well leakage) in active or suspended wells across Alberta using iterations of the machine learning algorithm, random forest. These two studies share commonality in that the formations located below potable groundwater but above typically targeted oil and gas formations, otherwise known as the intermediate zone, are identified as being critical to understanding both deep well leakage and the surface expression of well leakage. Results indicate that old abandoned wells at the study sites surveyed typically leave several intermediate zone aquifers commingled between abandonment plugs, increasing their susceptibility to the effects of nearby HF and EOR or SWD operations. For the surface expression of wellbore leakage the intermediate zone has generally been identified by previous studies as the origin for the majority of stray gas samples analyzed. While a modest predictive accuracy of 73% to 77% was achieved using random forest algorithms on a test dataset of wells across all of Alberta, predictive performance could likely be improved by including more predictors previously identified as being strongly correlated with well leakage that are related to the intermediate zone (e.g. well cement tops). In both studies the results can help shape future government regulations and policy but future field work on the intermediate zone is needed to improve our understanding of the integrity of oil and gas wells, the extent to which intermediate zone aquifers may be commingled by old abandoned wells, and by verifying the veracity of the existing database on the occurrence of SCVF/GM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".