Reservoir Characterization and Flow Simulation of a Low-Permeability Gas Reservoir: An Integrated Approach for Modelling the Tommy Lakes Gas Field
- Jack H. Deng (University of Calgary) | Roberto Aguilera (University of Calgary) | Mohammed Alfarhan (University of Texas at Dallas) | Lionel White (University of Texas at Dallas) | John S. Oldow (University of Texas at Dallas) | Carlos L.V. Aiken (University of Texas at Dallas)
- Document ID
- Society of Petroleum Engineers
- Journal of Canadian Petroleum Technology
- Publication Date
- May 2011
- Document Type
- Journal Paper
- 32 - 47
- 2011. Society of Petroleum Engineers
- 5.1.5 Geologic Modeling, 1.2.3 Rock properties, 5.6.1 Open hole/cased hole log analysis, 5.5.8 History Matching, 6.1.5 Human Resources, Competence and Training, 5.5.2 Construction of Static Models, 1.6.9 Coring, Fishing, 5.1 Reservoir Characterisation, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation
- simulation workflow, natural gas, hydraulic fracture, low permability
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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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