Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modelling the Tommy Lakes Gas Field
Bibliographic record
Abstract
Summary The Tommy Lakes field is located in northeastern British Columbia, Canada, and is one of the largest Middle Triassic gas pools within the western Canada sedimentary basin (WCSB). The major gas-production formation (Halfway/Doig reservoirs) at the Tommy Lakes field corresponds to shoreface sands with permeabilities ranging between 0.1 and 3 md, and porosities of 3 to12%. For the purpose of production optimization and field development, a full-field reservoir model was developed with the integration of advanced reservoir characterization, hydraulic-fracture modelling, and history-matching techniques. This study presents an integrated workflow for modelling the low-permeability Doig gas reservoir. A stochastic geostatistical reservoir model was developed on the basis of concepts emanating from an outcrop analogue analyzed with terrestrial light detection and ranging (LiDAR) technology and 60 wells that represent the fundamental rock characteristics, structure, facies? proportions, and petrophysical properties of the Doig anomalously thick sandstone bodies (ATSBs). Structural tops were interpreted from well logs and permeability/porosity relationships established from quantitative log analysis and core/log calibration. Facies were identified in cored intervals and were further grouped into four lithofacies. An artificial neural network (ANN) was used for training the logs of key wells [gamma ray (GR), neutron porosity (NPHI), and bulk density (RHOB)] and populating the facies distribution of uncored wells. Facies-based log-derived porosity, permeability, shale volume, and water saturation were assigned to gridblocks using sequential Gaussian simulation (SGS). Finally, the Monte Carlo simulation approach was used to rank the key variables affecting original gas in place (OGIP) in the uncertainty and optimization process. Flow-based techniques were used for upscaling reservoir properties into the coarse simulation grid. The full-field simulation model was calibrated with buildup data and hydraulic-fracture modelling of single wells. Production of the Doig channel from commingled wells was allocated systematically in order to achieve a good match of the gas-production history and bottomhole pressures. Sensitivity analysis of fracture half-length and its impact on ultimate gas recovery was investigated. This concluded with an integrated development strategy. It is concluded that integration of multiple domains leads to a valid full-field reservoir model, which is critical in developing an integrated strategy, predicting reservoir performance, and optimizing gas production.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".