Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modeling Tommy Lakes Gas Field
Bibliographic record
Abstract
Abstract 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 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 modeling, and history matching techniques. This study presents an integrated workflow for modeling the low permeability Doig gas reservoir. A stochastic geostatistical reservoir model was developed based on concepts emanating from an outcrop analog analyzed with terrestrial LiDAR technology and 60 wells that represent the fundamental rock characteristics, structure, facies proportions and petrophysical properties of the Doig Anomalously Thick Sandstone Bodies (ATSB). 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 4 lithofacies. An artificial neural network was used for training the logs of key wells (GR, NPHI, RHOB) and populating the facies distribution of uncored wells. Facies-based log-derived porosity, permeability, shale volume and water saturation were assigned to grid blocks using sequential Gaussian simulation (SGS). Finally, the Monte-Carlo simulation approach was used to rank the key variables affecting OGIP in the uncertainty and optimization process. Flow based techniques were used for up-scaling reservoir properties into the coarse simulation grid. The full field simulation model was calibrated with buildup data and hydraulic fracture modeling of single wells. Production of the Doig channel from comingled 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 concludes 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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".