Look Ahead of the Bit While Drilling: Potential Impacts and Challenges of Acoustic Seismic While Drilling in the McMurray Formation
Bibliographic record
Abstract
Summary The oil and gas industry, operating and service companies, and academia are actively searching for ways to look ahead of the drill bit while drilling to reduce the risks and costs of the operation and improve the well-placement process. Optimal drilling in challenging and highly heterogeneous reservoirs, where geological data cannot adequately constrain high-frequency variations in rock properties, requires reliable subsurface information from around and ahead of the drill bit. To provide this, we have developed a seismic-while-drilling (SWD) imaging algorithm using signal processing, drillstring modeling, and prestack wave-equation migration. To extend the visibility ahead of the bit, we use the drill bit as a seismic source and image the changes in acoustic properties of rocks both around and ahead of the drill bit. The common practice is to build reverse vertical seismic profile (R-VSP) gathers. Here, we use a blind deconvolution algorithm to estimate the drill-bit source signature from the data directly. Alternatively, we can estimate such a signature through drillstring modeling and surface measurements (i.e., hookload and hook speed). The drillstring dynamics are modeled and analyzed using Riemann's invariants and a backstepping approach in a field-verified model. Next, we enter the estimated source signature into the prestack wave-equation depth-imaging workflow. Our simulations show that providing the drill-bit source signature to the prestack wave-equation depth migration consistently delivers reliable subsurface images around and ahead of the drill bit. The output of our workflow is a high-resolution subsurface image, which is then applied to provide vital information in oil-sands reservoirs for placement of steam-assisted-gravity-drainage (SAGD) well pairs. Compared with conventional practices, the proposed methodology images around and ahead of the drill bit enable interactive decision making and optimal well placement. The key feature of the presented methodology is that instead of cross correlating the SWD data with the pilot trace and building R-VSP gathers, we use the estimated drill-bit source signature and deliver high-resolution prestack depth-migrated images. Through numerical modeling, we tested the potential impacts, validity, and challenges of the proposed methodology in drilling horizontal wells in SAGD settings with an emphasis on the McMurray Formation. We further compared the results with the conventional drilling practice. In contrast to existing tools that have limited depth of penetration, interpreting SWD data in real time confidently maps key target features ahead of the drill bit. This imaging workflow provides sufficient time to precisely control the borehole trajectory and stay within the desired reservoir zone. Accordingly, it mitigates the risk of intersecting mudstone-filled channels and lean zones.
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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 | 0.000 |
| 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".