Monday, October 22, 2018

Obtaining Reservoir Engineering Parameters in Each Layer

Once the reservoir geometry has been defined, if not actually computed, one step remains before synthesizing the complete reservoir model. This is the estimation of key reservoir engineering parameters in each defined interval across the areal extent of the reservoir. Key parameters are net thickness, porosity, oil, gas and water saturations, and horizontal and vertical permeabilities. The computation proceeds in two stages. 

First, in each well the parameters must be averaged for each interval from the petrophysical interpretations.  This is performed in the component property module and relies on careful selection of cutoffs to exclude sections of formation that do not contribute to fluid movement. Choice of cutoffs is made with the help of sensitivity plots showing how the averaged parameter varies with cutoff value, and preferably in a well with well-test data to validate the cutoff choices. 

Second, the averaged parameters for each interval must be gridded or mapped across the reservoir. In the log property mapping module, the RM package brings into play powerful algorthms that use seismic data to guide the mapping. The key to the method is establishing a relationship at the wells between some attribute of the seismic data and a combination of the averaged well parameters, and then using the relationship to interpolate the averaged parameter everyhwere in the reservoir. The seismic attribute could be amplitude, or acoustic impedance calculated earlier using the inversion module, or one of several attributes that are routinely calculated on seismic interpretation workstations and the imported to the RM system, or simply depth.

The relationship may be linear - that is, the combination of averaged parameters is defined as a simple weighted sum of seismic attributes -or nonlinear, in which an elaborate neural network approach juggles several linear relationships at the same time, picking the best one for given input. Linear relationships easily handle smooth dependecies such as between acoustic impedance and porosity. The nonlinear approach is required for averaged parameters, such as saturations, that may vary abruptly across a field.


In practice, the log property mapping module guides the interpreter through the essential stages: choosing the interval to map, comparing seismic data at the well intersections with the averaged well data, establishing relationship that show a good degree of correlation and then proceeding with the mapping. The advantage of log property mapping over conventional mapping was demonstrated in both the Conoco Indonesia, Inc. and Pertamina Sumbagut case studies. Research continues into finding ways of using all available data to assist the mapping of log data across the reservoir.





Building the Reservoir Model and Estimating Reserves

The stage is set for the RM package- the Model Builder. This module fully characterizes the reservoir by integrating the geometric interpretation  established with the correlation and section modeling modules, including definitions of reservoir tanks and fluid levels, with the reservoir engineering parameters established using the component property and log property mapping modules.

 The main task is constructing the exact shape of the reservoir layers. This is achieved by starting at a bottom reference horizon and building up younger layers according to their assigned descriptors, mimicking the actual process of deposition and erosion. For example, if a layer top has been defined as sequential and conformable, it will be constructed roughly parallel to the layer's bottom horizon. If a reference horizon has been described as an unconformity, then underlying layers can approach it at any angle, while layers above can be constrained to track roughly parallel. 


The areal bounds on layers are determined within the model builder module by severeal factors. First, spesific geometries can be imported. Second, areal bounds may be implied through the geometries created with the section modeling module. Third, the contours of petrophysical parameters estabished during log property mapping can establish areal limits. Fourth, thickness maps of layers can be interactively created and edited prior to model building.






The key dividen of model building is the establishment of reserve estimates for each tank. Oil in place, total pore volume, netpay pore volume, water volume, reservoir bulk volume, net-pay area and net-pay bulk thickness are some of parameters that can be calculated and tabulated on the workstation. Conoco Indonesia Inc.'s estimates using the RM package were in close agreement with standard calculation procedures. During appraisal, when the oil company decides whether to proceed to development, establishing reserve estimates is crucial. As a result, the many steps leading to this moment will be reexamined and almost certainly rerun to assess different assumptions about the reservoir. 


Say, for example, a geologist is working on correlating logs and creating geologic tops, while the geophysicist is preparing an inversion to obtain acoustic impedance. If both want to work concurrently, the version manager simply grows two branches. 

Similiarly, a reservoir engineer may wish to try several scenarios for mapping the distribution of porosity within a layer-say by mapping well log values only and alternatively by using seismics to guide the mapping with the log property mapping module. Two versions can be made in parallel with a branch for each scenario. Several further steps along each interpretation path may be necessary before it becomes clear which mapping technique is better. 

Material Balance Analysis and Preparation for Simulation

For reservoir managers striving to improve the performance of developed fields - for example, investigating placement of new wells or reconfiguring existing producers and injectors to improve drainage- the RM package has two more modules to offer. One provides a sophisticated material balance analysis that assesses whether the established reservoir model is compatible with historical production data.  The second converts the reservoir model into a format suitable for simulating reservoir behavior and predicting future production.


 Material balance analysis is performed using Formation reservoir test system module. In traditional material balance analysis, reservoir volume is estimated by noting how reservoir pressure decreases as fluids are produced. The more fluids produced, the greater the expected pressure decrease. Exactly how much depends on the compressibility of the fluids, which ban be determined experimentally from down-hole samples through pressure-volume-temperature (PVT) analysis, the compressibility of the rock, which can be determined from core samples in the lab, and , of course, reservoir volume. Faster declines in pressure than expected from such an analysis might indicate a smaller reservoir than first thought. Slower declines might indicate a high-volume aquifer driving production, or less rarely, connected and as yet undiscovered extensions to the reservoir. This traditional anaysis of reservoir size and drive mechanism requires no a priori knowledge of reservoir geometry, only production, pressure and PVT data. 


The module uses these basic principles of material balance, but applies them within the geometrically defined reservoir tanks of established reservoir model. This allows not only verification of tank volumes, but also estimation of fluid communication between tanks. Communication between tanks could be due to an intervening low-permeability bed or a fault being only partially sealing. Another result is the prediction of how fluid contacts are moving.










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