Monitoring and Modelling in Coupled Geomechanics Processes
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Bibliographic record
Abstract
Abstract Geomechanics issues are vital in all reservoir processes, but particularly so in weak, unconsolidated sandstones. Coupled stress-flow simulation is necessary to analyse and understand effects such as changes in reservoir volume that arise from heating and pressurization, and non-linear plasticity models that incorporate shear dilatancy are needed to simulate the dilation effects that are observed in any thermal process in sands. Coupling of flow and stress is based on the volume changes that arise with changes in pressur and temperature. Incorporating shear dilation is based on computation of effective stresses from ΔT andΔp, then assessing the state of the rock to see if and how much it shears and dilates. These processes are ill-quantified at present, so it is necessary to monitor. The two monitoring domains that are of greatest interest to coupled geomechanics simulation are the deformation field and the seismic attributes field. More specifically, how these fields evolve in space and with time are the key factors to tracking processes to calibrating geomechanics models, and to the successful optimization of complex in situ processes. A general geomechanics view of how to achieve process monitoring and optimization goals is presented in a general fashion. Further progress in the areas of monitoring, inversion, and geomechanics simulation is needed, although recent developments have advanced these aspects. Introduction Conventional monitoring in petroleum engineering addresses pressure, temperature, and rate measurements, as well as providing some data collected by wellbore logs such as temperature or rate surveys (e.g., spinner surveys). Oil, gas and water production and injection rates are required for both regulatory and engineering purposes to help calculate saturations and recovery factors (RF). Changes in reservoir response were commonly assessed using classical well tests and analyses.[i,ii] Because classic reservoir simulation in conventional low-viscosity cases deals only with Darcy and Fourier mass and heat transport, as well as saturation and relative permeability calculations, these measures were deemed sufficient for reservoir management. Flow rates from meters (multiphase) and well capacities based on well testing, combined with data on facilities capabilities, are also used for production optimization. iii These measures are insufficient for more challenging cases such as heavy oil (HO) thermal extraction, HPHT reservoirs, highly compacting reservoirs, and new gravitationally dominated technologies. In these cases, we may be interested in new measures, such as the reservoir volume change (ΔV), gas saturation change in situ (ΔSg), swept volume, and so on. These cannot be measured by conventional p-T-Q methods or even geophysical wellbore logging. Furthermore, in cases of major changes of properties in situ, involving shear dilation, compaction, or fracturing, understanding what is happening requires different monitoring and simulation methods. New monitoring measures have emerged in use or are now in a field research mode, based on direct, indirect, or remote measurements. Furthermore, with increasing computing power, several methods which have remained underutilized should soon see application in the field.
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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) | 0.000 | 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 it