Effect of Bedding and Drilling-Induced Stresses on Borehole Sonic Logging
Bibliographic record
Abstract
Advancements in the design of sonic logging tools have made it possible to characterize rock formations more extensively. This achievement has had a great impact on the design of effective drilling, completion and production practices. However, interpretation of advanced sonic logging tools is complex, and the relative contributions of intrinsic and stress-induced elastic property anisotropy on tool response are not well understood. This thesis presents a methodology for predicting sonic logging tool response accounting for the effects of bedding and drilling-induced stresses, based on anisotropic and stress-dependent dynamic and static elastic properties. \nIn this project, boreholes from two areas were studied: Northeast British Columbia (Montney Formation) and Southeast Saskatchewan (Deadwood Formation). Samples provided from these boreholes were tested by laboratory technical staff under hydrostatic and uniaxial loads, and these results were used to predict the stress dependence of all five dynamic and static elastic moduli comprising the transversely isotropic stiffness tensor. \nThe static elastic properties as a function of stress (acquired from lab testing results) were utilized to define the static elastic stiffness tensor, and static stress analysis was conducted to predict the stress alteration around the borehole. The results of this static stress analysis were then used in conjunction with dynamic elastic properties (defined as a function of stress) to determine dynamic elastic stiffness properties of the rock around the borehole. These dynamic properties were used as inputs for dynamic (wave propagation) modeling.\nThe modeled acoustic waveforms were recorded for each simulation. The results were used as input for a codes written in Matlab to generate dispersion curves. Simulation outputs were compared to field-based logging results, in terms of dispersion curve appearance and shear wave velocity anisotropy. \nThe results of comparison between simulated and field results showed a similarity in the general form of the results, but differences in the absolute values of velocities. Because the modeling tools (for simplified scenarios) were tested against analytical solutions, and favourable comparisons were observed between predicted velocities based on simulation results and values taken directly from experimental results, the difference between field and simulated results are believed to result from differences between lab testing conditions and in-situ conditions such as temperature, frequency, size (dimensions), pore fluid properties, pore pressure, and rock property heterogeneity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".