Integrated study of a fractured granitic basement reservoir with connectivity analysis and identification of sweet spots: Cauvery Basin, India
Bibliographic record
Abstract
Abstract Basement exploration in India has seen increased interest after the recent discovery of a field in the Cauvery Basin in southeastern India, with an average individual well production of 700 b/d from a fractured basement reservoir. The field is presently under development, with several development well locations identified for drilling. Optimized development of a fractured basement reservoir requires identification of areas with a permeable fracture network. To meet this objective, we adopted a comprehensive integrated workflow involving the use of common reflection angle migrated seismic data, fracture modeling, a 1D mechanical earth model (MEM), identification of critically stressed fractures in 3D space, fracture permeability/connectivity analysis, and sweet spot identification. The workflow yielded a robust discrete fracture network model based on 3D directional fracture intensity, a 1D MEM that gave regional stress gradients (pore pressure, overburden, Shmin, and SHmax), and rock strength and elastic properties. In addition, we generated a critically stressed 3D fracture model and performed sequential stratal surface restoration for predictive strain modeling that was calibrated at wells. Our fracture permeability and connectivity analysis showed that existing hydrocarbon-producing wells are located within areas that have a fracture cluster/swarm with associated good fracture connectivity. A 3D basement facies model constructed by integrating well data and a poststack inversion impedance volume showed that major flow zones occur in weathered basement associated with low impedance. This model, in combination with fracture intensity data, provides good indication of the location of basement sweet spots in the Cauvery Basin. The understanding gained on the controls of occurrence of basement fractures explains why some wells in the field are producers and others are dry. This led to greater confidence in optimizing the locations of previously proposed new development 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".