Numerical Modelling Of Advanced In Situ Recovery Processes In Complex Heavy Oil & Bitumen Reservoirs
- Colin Charles Card (Phoenix Consultants) | Jason Christopher Close (Computer Modelling Group Inc) | David Albert Collins (Computer Modelling Group Inc) | Peter H. Sammon (Computer Modelling Group Ltd.) | Thomas James Wheeler (ConocoPhillips Co) | Noble G. Fortson (IBM Corp.)
- Document ID
- Society of Petroleum Engineers
- SPE International Thermal Operations and Heavy Oil Symposium, 1-3 November, Calgary, Alberta, Canada
- Publication Date
- Document Type
- Conference Paper
- 2005. SPE/PS-CIM/CHOA International Thermal Operations and Heavy Oil Symposium
- 5.8.5 Oil Sand, Oil Shale, Bitumen, 1.2.2 Geomechanics, 5.5.1 Simulator Development, 5.5.8 History Matching, 5.3.4 Integration of geomechanics in models, 4.1.5 Processing Equipment, 7.2.1 Risk, Uncertainty and Risk Assessment, 1.6 Drilling Operations, 4.3.4 Scale, 4.1.2 Separation and Treating, 5.3.9 Steam Assisted Gravity Drainage, 2.4.3 Sand/Solids Control, 2.2.2 Perforating, 5.1.5 Geologic Modeling, 5.5.3 Scaling Methods, 5.4.6 Thermal Methods, 5.5 Reservoir Simulation
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As development activities in heavy oil and in situ bitumen deposits have accelerated, the challenge of forecasting the performance of in situ recovery processes at field scale has increased exponentially.
Delineation drilling results make it apparent that these deposits are highly complex and three-dimensionally heterogeneous.Heterogeneity has a significant impact on the effectiveness and economics of the recovery process.
Many experienced operators are recognizing that in addition to the static complexity of the reservoirs it is necessary to consider the dynamic stress state in the regions undergoing production.Geomechanical factors are significant and must be built into any realistic numerical simulation of recovery processes.
It has become apparent to operators that modeling single well-pair operations may be misleading, and seven to ten well-pair models are now quite common.
All these factors result in increasing size and complexity of numerical simulation models.
Reservoir simulator developers have responded with two technologies to achieve reasonable run times in these large and complex models.The combined use of 64-bit symmetrical multiprocessor computers and dynamic grid refinement will be discussed and compared against traditional simulation methods.
This paper will provide examples of the application of these leading edge technologies for in situ oil sands development in the Surmont area of the Athabasca deposit.
The investigation discussed in this paper began with a 3D "STATIC FINE" (or SF) simulation model of a typical 9-wellpair ½-pad, gridded in the I, J, K (vertical) directions with 45x1x1m grid cells.This is the base simulation model.It was statically gridded to accurately model the thermal and flow regimes that occur perpendicular to the well paths, i.e. in the cross-sectional plane.Such a model requires 64-bit address space with memory requirements of approximately 16 GB of RAM.The forecast results and performance of this finely gridded model formed a base for comparison with the results obtained from all other models.
This model was coarsened in the J direction to 45m so that the resulting model would fit within the 32-bit environment of desktop PC's, i.e. memory requirements for the model cannot exceed 3 GB of RAM.This model was designated the "COARSE" grid model.
The results of a forecast of reservoir performance with this model were compared to those obtained from the SF model.
Large models such as the SF model take a long time to run in serial or single-processor mode.We therefore extended the investigation with this model to cover the use of parallel processing, using up to 8 CPU's in a shared memory, symmetric multiprocessor (SMP) environment.This approach also took advantage of IBM's simultaneous multithreading (SMT) technology.
Static gridding of these models is wasteful of resources and time.The fine grid is only required in areas of the model where there are substantial changes in variables - temperature, saturation, viscosity, flow rate, and pressure for example - over relatively short distances (< 5 meters).
To address this, the technique of dynamic gridding was applied to the SF model and this DYNAMIC FINE gridded model (or DF model) was run in serial (single processor) mode using a 64-bit machine.The run time for this model was compared with that for the SF model, for equivalent forecast results.
Finally the DF model was run in parallel mode for additional speed up and comparable simulation results.
The above models attempt to model the effects of stress on reservoir properties such as porosity and permeability using pressure dependent porosity in conjunction with static permeability multipliers.To address these effects further, a 2 well-pair finely gridded element of symmetry model was set up as a base case and four different approaches to modeling the effect of stress on porosity and permeability were investigated.
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