Improved In-Situ Stress Characterization Through Analysis of Diagnostic Fracture Injection Tests DFITs Using the Changing Compliance Method and its Impact on Caprock Integrity Analysis
Bibliographic record
Abstract
Abstract Diagnostic Fracture Injection Tests (DFITs) provide a critical piece of information on the competency of the top seal for injection projects and are therefore essential for Subsurface Containment Assurance (SCA) and Maximum Operating Pressure (MOP) determination. Results of DFITs provide the best estimate for the minimum principal stress component, which is a major input for tensile and shear failure tolerance analysis. Therefore, best practices in execution and analysis of DFITs is extremely important for safe and successful operations. Numerous DFITs have been conducted over the past ten years in Surmont to assess the integrity of the caprock and determine the maximum allowable steam injection pressure. A combination of surveillance and coupled reservoir-geomechanics simulations have been utilized to maintain a minimum of 20% safety factor for caprock failure. The tangent analysis method for determining fracture closure pressure has been extensively used in the past decade for DFIT analysis in the industry [1 and 2]. The validity of this method has been challenged recently through more rigorous modeling of the hydraulic fracturing process and field observations. An alternative analysis method referred to as the compliance method has been proposed and successfully used in many cases [3]. In the past few years, we have demonstrated the potential to safely increase the maximum operating pressure and its impact on production, through extensive caprock integrity analysis in Steam Assisted Gravity Drainage (SAGD) operations [4 and 5]. In this study, the DFITs carried out in Surmont were revisited to assess the potential impact of characterizing in-situ stresses with improved accuracy on maximum allowable injection pressure and ultimately production uplift. The analysis shows higher minimum horizontal stresses in the caprock are supported by field observations, which could potentially allow for higher injecting pressure and close to five percent additional production in the two selected SAGD pads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".