A Semi-Analytical Geomechanical Approach for Forecasting Production Performance in Multifractured Composite Systems
Bibliographic record
Abstract
Abstract Stress-dependence of reservoir matrix and fractures can strongly affect the performance of multifractured horizontal wells (MFHWs) completed in unconventional hydrocarbon reservoirs. In order to model fluid flow in unconventional reservoirs exhibiting this stress-dependence, most traditional reservoir flow simulators, and many simulators described in published work, use conventional reservoir fluid flow model formulations. These formulations typically neglect the influence of the rate of change of volumetric strain of the reservoir matrix and fractures, even though reservoir stress and pressure change significantly during the course of production. As a result, the effect of matrix and fracture deformation on production is neglected, which can lead to errors in predicting production performance in most stress-sensitive reservoirs. To address this problem, some studies have proposed the use of porosity and transmissibility multipliers to model stress-sensitive reservoirs. However, in order to apply this approach, multipliers must be estimated from laboratory experiments, or used as a history-match parameter, possibly resulting in large errors in well performance predictions. Alternatively, fully-coupled, fully numerical geomechanical simulation can be performed, but these methods are computationally costly, and models are difficult to setup. This paper presents a new fully-coupled, two-way analytical modeling approach that can be used to simulate fluid flow in stress-sensitive unconventional reservoirs produced through MFHWs. The model couples poroelastic geomechanics theory with fluid flow formulations. The two-way coupled fluid flow-geomechanical analytical model is applied simultaneously to both the matrix and fracture regions. In the proposed algorithm, a porosity-compressibility coupling parameter for the two physical models is setup to update the stress- and pressure-dependent fracture/matrix properties iteratively, which are later used as input data for the fracture-matrix reservoir fluid flow model at each iteration step. The analytical approach developed for the fully-coupled, two-way analytical model, using the enhanced fracture region conceptual model, is validated by comparing the results with numerical simulation. Predictions using the fully-coupled enhanced fracture region model are then compared with the same enhanced fracture region model but with the conventional pressure-dependent modeling approach implemented. A sensitivity study performed by comparing the new fully-coupled model predictions with and without geomechanics effects accounted for reveals that, without geomechanics effects, production performance in stress-sensitive reservoirs might be overestimated. The study also demonstrates that use of the conventional stress-dependent modeling approach may cause production performance to be underestimated. Therefore, the proposed fully-coupled, two-way analytical model can be useful for practical engineering purposes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".