Application of Wireline Stress Testing for SAGD Caprock Integrity
Bibliographic record
Abstract
Abstract Measuring geomechanical properties of the caprock and shale gas reservoirs for enhanced oil recovery (EOR) or storage reservoirs has become an integral part of asset evaluation services. The steam-assisted gravity drainage (SAGD) process, as well as other EOR and storage applications (gas, nuclear waste, CO2), requires caprock integrity testing. Measuring in-situ stresses is an important input to caprock integrity solutions not only for the technical management of projects but also for environmental reasons. A Wireline Formation Testing (WFT) tool is one of the commonly used techniques used for direct measurement of the minimum in-situ stress at different depths. The process is commonly called a mini-frac or micro-frac and is typically performed in an openhole wellbore. A suitable tool string includes a straddle packer arrangement, downhole pump, gamma ray sonde for depth correlation, motorized valves, and pressure gauges. To perform a stress test, an interval of the wellbore is isolated by inflating the straddle packer arrangement. The interval is then pressurized by pumping fluid until a tensile fracture is initiated. In an open hole, the fracture initiates and propagates normal to the minimum stress. Multiple injection and fall-off cycles are performed to ensure fracture growth beyond the hoop stress regime. The data is analyzed to determine fracture initiation pressure, fracture propagation, and closure pressure. This paper describes the process of minimum in-situ stress measurement using a WFT. Lessons learnt over the years and best practices are highlighted along with their importance for proper job planning. Case studies of WFT testing including SAGD caprock stress testing from Canadian fields are presented and discussed.
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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 | 0.000 |
| 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) | 0.002 | 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".