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 analyze and understand effects such as changes in reservoir volume that arise from heating and pressurization. Also, non-linear plasticity models incorporating shear dilatancy are needed to simulate the dilation effects that are observed in thermal extraction processes in unconsolidated sands. Stress-flow coupling is based on the volume changes that arise with pressure and temperature changes (?p, ?T). Incorporating shear dilation is based on computation of effective stresses from ?T and ?p, then assessing the state of the rock to see if it is shearing and by how much it must dilate. These processes are poorly quantified at present, so it is necessary to monitor the process to calibrate simulation models. The two monitoring domains of greatest interest to coupled geomechanics simulation are the deformation field and the seismic attributes field. How these fields evolve in space and with time are the key factors to tracking processes, to calibrate geomechanics models and to successfully optimize complex in situ processes. A general geomechanics view of how to achieve process monitoring and optimization goals is presented here. Though recent developments have been promising, further progress in monitoring, inversion and coupled geomechanics simulation is needed. Introduction Conventional monitoring in petroleum engineering addresses pressure, temperature and rate measurements, as well as 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 regulatory purposes and to help calculate saturations and recovery factors (RF). Changes in reservoir response were commonly assessed using classical well tests and analyses(1, 2). Because classic reservoir simulation in conventional low viscosity cases deals only with mass and heat transport (Darcy and Fourier diffusion processes) combined with saturation changes and relative permeability calculations, these measures were deemed sufficient for reservoir management. Flow rates (Q), well test data and facilities capability analyses are also used for production optimization(3). These measures are considered insufficient for heavy oil (HO) thermal extraction, HPHT reservoir management, high compaction cases and gravitationally-dominated production technologies. In such cases, we are more interested in measures such as the reservoir pore volume change (?V), gas saturation changes in situ (?Sg), swept volume distribution, and so on. These cannot be measured by conventional p-T-Q methods or geophysical wellbore logging, nor are they easily amenable to calculation. When shear dilation, compaction or induced fracturing take place, major changes in rock mass properties occur; understanding what is happening and where it is taking place requires different monitoring and simulation methods. To make monitoring data more useful, flow-stress coupled modelling, also referred to as coupled geomechanical modelling, is carried out. Changes in p and T are analyzed in terms of effective stress changes (? s'ij) through their effect on rock and pore volumes. The links between ?V and the changes ?p, ?T and ? s'ij are established through p-, T-, s'-compressibilities of the bulk rock and of the mineral matter comprising the matrix.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".