A Hierarchical Multiscale Framework for History Matching and Optimal Well Placement for a HPHT Fractured Gas Reservoir, Tarim Basin, China
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Bibliographic record
Abstract
Abstract History matching of million-cell reservoir models still remains an outstanding challenge for the industry. This paper presents a hierarchical multi-scale approach to history matching high resolution dual porosity reservoir models using a combination of evolutionary algorithm and streamline method. The efficacy of the approach is demonstrated through application to a high pressure high temperature (HPHT) fractured gas reservoir in the Tarim basin, China with wells located at an average depth of 7500 meters. Our proposed multi-scale history matching approach consists of two-stages: global and local. For the global stage, we calibrate coarse-scale static and dynamic parameters using an evolutionary algorithm. The global calibration uses coarse-scale simulations and applies regional multipliers to match RFT data, well bottom hole pressures, and field average pressure. For the local stage, we calibrate fracture permeability using streamline based sensitivities to further match well bottom-hole pressures. The streamlines are derived from the fracture cell fluxes and the sensitivities are analytically computed for highly compressible flow. The sensitivities are validated by comparison with the pertubation method. The proposed hierarchical multiscale history matching workflow is applied to a faulted and highly fractured deep gas reservoir in the Tarim basin, China. The excessive well cost arising from the large well depth (7500 meters) and high pressure (18000 psi) necessitates optimal field development with limited number of wells. The fracture properties of dual porosity model are upscaled from a highly dense discrete fracture network model generated based on well data and seismic attributes. The history matching includes RFT data, static pressure data and flowing bottom-hole pressure data in producing wells. Field average pressure and RFT (static pressure) data were well matched during the global stage using coarse scale models while flowing bottom-hole pressure is further matched during the local stage calibration using fine scale models. Streamline method has been applied previously mainly to incompressible or slightly compressible flow. However in this application, the results show that the modified streamline-based sensitivity can also significantly reduce data misfit for highly compressible flow. The history matched models are used to visualize well drainage volumes using streamlines. The well drainage volumes in conjunction with static reservoir properties are used to define a ‘depletion capacity map’ which is then used for optimal infill well placement. The novelty of our approach lies in the application of streamlines derived from dual porosity finite-difference simulation to facilitate history matching and well placement optimization in a tight gas reservoir. The newly developed streamline-based analytical sensitivities are suitable for highly compressible flow. To our knowledge, this is the first time streamlines have been used to facilitate history matching and optimal well placement for gas reservoirs.
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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 | 0.001 |
| 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