Velocity, attenuation, and microseismic uncertainty analysis of the Niobrara and Montney reservoirs
Bibliographic record
Abstract
Time-lapse reservoir characterization with surface seismic provides greater spatial information about reservoir physical properties, and delineates reservoir scale changes. Identification of reservoir deformation due to hydraulic fracturing and production improve reservoir models by mapping non-stimulated and non-producing zones. Monitoring these time-variant changes improves the prediction capability of reservoir models, which in turn should lead to improved well and stage placement. In Wattenberg Field, the Reservoir Characterization Project (RCP) at the Colorado School of Mines (CSM) and Anadarko Petroleum Corporation (APC) collected time-lapse, multicomponent seismic data in order to characterize the reservoir fracture changes caused by hydraulic fracturing and production in the Niobrara Formation and Codell Sandstone member of the Carlile Formation. Three seismic surveys help understand the dynamic reservoir changes caused by hydraulic fracturing and production of eleven horizontal wells within a one-square mile section (Wishbone Section). A baseline survey was recorded immediately after the wells were drilled, another survey after stimulation, and a third survey after two years of production. A robust layer stripping method is used to quantify 4D velocity and attenuation from pre-stack seismic data. Processing of the data before attenuation analysis includes noise reduction, regularization of amplitudes, and statics. Data show that time-lapse, pre-stack velocity and attenuation estimates are sensitive to hydraulic stimulation and production. Time-lapse velocity and attenuation results are integrated with image logs, surface microseismic, tracer data, and production information to analyze how faults, joint sets, and well spacing, affect stimulation, early term production, and late term production of the eleven horizontal wells in the Wishbone Section. Data demonstrate that faults in the reservoir limit lateral stimulation and allow hydraulic fracture fluids to move to other reservoir facies vertically within the Wishbone Section. Attenuation and velocity changes are observed in the western portion of the survey. Higher producing wells are also located the western portion of the study area. Borehole microseismic is a common tool used to evaluate hydraulic stimulation. A challenge in microseismic monitoring is quantification of survey acquisition and processing error, and how these errors jointly affect estimated locations. Quantifying error and uncertainty has multiple benefits, such as more accurate and precise estimation of locations, anisotropy, moment tensor inversion, and, potentially, allowing for detection of 4D reservoir changes. Processing steps are applied to a downhole microseismic dataset from Pouce Coupe, Alberta, Canada. A probabilistic location approach is implemented to identify the optimal bottom well location based upon known source locations. Probability density functions (PDF) are utilized to quantify uncertainty and propagate it through processing, including in source location inversion to…
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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 | 0.001 |
| 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 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".