Geological Carbon-Storage Site Screening for Indian Sedimentary Basins
A completed geology study screening all 26 Indian sedimentary basins and comparing ten priority basins through transparent evidence, confidence, missingness, decision weights, and uncertainty.
Software compatibility
The released results use the frozen 29 August 2026 evidence register, Python package constraints, 25,000 uncertainty samples per profile, and random seed 20260829. New evidence or model choices require a fresh review and validation run.
Project definition
Problem statement
India has substantial public carbon-storage resource estimates, but basin-scale values differ in scale, maturity, definition, evidence quality, and missing data.
The geology problem is to identify defensible research priorities without presenting regional resource figures as formation-scale capacity or site approval.
Project objectives
- Build a frozen Tier 1 register for all 26 Indian sedimentary basins.
- Compare ten selected basins through seven documented evidence criteria.
- Separate raw rankings from confidence and missing-evidence adjustment.
- Test the shortlist under four decision profiles and fixed-seed uncertainty.
- Define the staged geological work required before formation-scale decisions.
Project structure
Project components
National basin register
Records basin names, DGH categories, areas, published saline-storage values, selection status, and missingness.
Tier 2 evidence model
Scores resource evidence, injectivity evidence, containment evidence, source match, infrastructure, constraints, and data maturity under one declared rubric.
Confidence adjustment
Keeps raw scores visible while applying explicit evidence-confidence and missingness penalties.
Uncertainty analysis
Runs 25,000 seeded samples for each of four weight profiles and reports leadership, top-three stability, median rank, and score intervals.
Characterisation plan
Connects basin screening to evidence consolidation, data audit, regional modelling, source screening, dynamic appraisal, and risk and monitoring design.
Methodology
Project workflow
- 01Freeze the evidence
The source register records what each public source contributes, its limitation, access date, and use.
- 02Build Tier 1
All 26 basins remain visible, including seven basins without a reported saline-storage value.
- 03Score Tier 2
Ten basins are compared through the same seven-criterion rubric without inventing formation properties.
- 04Adjust and sample
Raw scores, confidence penalties, four profiles, and uncertainty samples produce separate evidence views.
- 05Plan the next work
The final shortlist is tied to explicit data-acquisition and characterisation gates.
Demonstration scenario
Saurashtra leads the raw balanced ranking. Assam Shelf leads after confidence and missing-evidence adjustment, followed by Assam-Arakan Fold Belt and Saurashtra. The change is retained as a central result because evidence maturity materially affects the research priority.
Engineering
Tools and method
- Tools
- The project uses Python, NumPy, Pandas, Matplotlib, QGIS, Jupyter for subject analysis, simulation, and results.
- Evidence layer
- Sixty-six screened references support the basin register, capacity terminology, criterion design, uncertainty, standards, and comparison.
- Decision layer
- Python, NumPy, and Pandas implement the ordinal rubric, four normalized weight vectors, confidence adjustment, and ranking.
- Uncertainty layer
- A fixed random seed drives 100,000 total samples across the four profiles while retaining every reported interval and rank statistic.
- Evidence output
- CSV, JSON, PNG, PDF, and editable Word files retain the inputs, results, figures, references, and interpretation.
- Verification
- Automated tests, repository validation, document accessibility checks, PDF checks, and a Linux container run verify the release.
Testing
Evaluation
Evaluation measures
- Completeness of the 26-basin national register
- Raw and confidence-adjusted score and rank under each profile
- First-rank share, top-three share, median rank, and score interval
- Sensitivity of the shortlist to geological, source, infrastructure, and constraint priorities
- Visibility of confidence, missingness, and decision boundaries
- Completeness of the six-stage follow-up characterisation plan
Project boundaries
- Published basin-scale values are screening resources and are not formation-scale site capacities.
- DGH categories describe hydrocarbon exploration maturity and are not carbon-storage suitability classes.
- Tier 2 scores are ordinal evidence positions, not measured reservoir, seal, pressure, or geomechanical properties.
- The uncertainty analysis describes the released decision model and does not calculate geological failure probability.
- The project does not select a drill-ready site, certify capacity, design an injection operation, grant approval, or provide permission to inject.
Included
- 01Complete Python source code
- 02Tier 1 register for all 26 Indian sedimentary basins
- 03Tier 2 evidence comparison of ten basins using seven criteria
- 04Four decision-weight profiles and 25,000 uncertainty samples per profile
- 05Seven CSV result tables, one JSON summary, and twelve labelled figures
- 0681-page project report in PDF and editable Word formats
- 0716-page setup and usage guide in PDF and editable Word formats
- 0866 screened and annotated references with a source matrix
- 09Four automated tests, repository validation, and a Docker workflow
Project record
No information is collected on this page.
- Permanent project ID
- GP-GE-1L1427M
- Catalogued
- 21 Aug 2026
- Completed
- 30 Aug 2026
- Verified
- 30 Aug 2026
- Demonstration
- Included in repository
Handover
After purchase
- 01Payment is confirmed
The project is marked unavailable and cannot be purchased again.
- 02Repository access is granted
The buyer's submitted GitHub account receives access to the private repository.
- 03The purchase record is delivered
The certification sheet is prepared from the reviewed buyer details and sent privately by email.