The Success Story of Acid Gas Injection (AGI) in WCSB
Bibliographic record
Abstract
Deep zone disposal of acid gas (H2S + CO2) into saline aquifers and depleted gas/oil reservoirs has been successfully practiced in the Western Canada Sedimentary Basin (WCSB) over the past three decades. The WCSB is uniquely positioned in Canada as it offers striking resources for hydrocarbon production, albeit some being sour, as well as attractive candidates for H 2 S and CO 2 disposal. Nevertheless, acid gas injection (AGI) schemes in the WCSB still face technical and administrative challenges. First and foremost, the identification of suitable storage sites which pass the source–sink hub criteria has not been fully developed. Then, a list of required tests and data for the assessment of reservoir injectivity has to be formulated. Finally, the application and approval mechanisms are somewhat complicated and onerous. The number of applications required varies with the starting point for the proposed AGI scheme. In the situation where the applicant/proponent does not own the mineral rights in the target zone, the applicant will have to either obtain authorization or consent from the Crown or permission from the mineral rights holder on the active agreement, either the freehold mineral owner or the lessee. From there, the applications can be broken down into four categories: well/facilities, reservoir management, wellbore integrity, and emergency planning and risk management. Comprehensive and thorough engineering assessment is required for each category. Generally, the reservoir management application informs the regulator of the details of the proposed AGI, justification for the scheme, suitability of the zone, and ability of the bounding formations to contain the disposal volumes in the zone intended and is fundamental in the process. This paper's aim is to present a best practice workflow, which is a collective understanding of over 30 years of applications/operations in Alberta, British Columbia, and Saskatchewan, for identification and approval of AGI scheme operations. In addition to the requirements defined by AB, BC, and SK governments, a four-stage screening and two-level ranking tool has been developed, which enables operators to identify top candidates for AGI operation. For maximum wellhead injection pressure (MWHIP), storage potential, and injectivity assessment, the workflow couples compositional reservoir modeling with compositional wellbore modeling to identify the state of the AG at sandface, the size of AG plume, the reservoir pressure as well as the volume of disposed and stored AG over the life of the project. Specific guidelines for wellbore design are also provided. Due to increasing concerns over emissions that are generated by all sectors of industry, governments at all levels are seeking solutions to the permanent disposal of those volumes. H 2 S and CO 2 are the most abundant compounds in the emissions and are being targeted for large-scale projects to permanently be disposed or sequestered in safe zones. CO 2 sequestration is basically AGI without the H 2 S. The decades of experience with AGI that industry and the regulators have in the WCSB place the WCSB in the forefront of developing successful CO sequestration projects that are safe, permanent, and environmentally prudent. The 2 overall workflow will require operators to comply with government requirements, minimize the risk of AGI operation, and maximize the economic return by the selection of top disposal formation candidates.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".