A New Stimulation Process
A new process for matrix acid stimulation relies heavily on the Virtual Lab software. Mineral-fl uid reactions are simulated quickly and effi ciently, so the best treatment option can be selected. Schlumberger has developed several databases in a proprietary data archive to use with the simulator. The design process starts with a collection of well data (next page). Mineralogy, which is an important parameter for proper stimula- tion design, can be obtained from X-ray diffraction of core material. The other data include well completion, formation tempera- ture, porosity, permeability, evidence relating to formation damage, and well history. Schlumberger has created an extensive database of reaction kinetics and thermo- dynamics, but occasionally some specifi c kinetics parameters are not available. In that case, reactions monitored in a controlled envi- ronment, a batch-reactor, provide necessary data. The new results are added to the database. As the next step, experts recommend per- forming at least one fl ow test using core material relevant for each formation to be stimulated. These core tests are also stored in the database, so a new test is not necessary if results are already available. If they are not available, and suitable core material can be obtained, then a fl ow test should be per- formed to provide data for the Virtual Lab simulator to match mineral surface area and the permeability-porosity relationship for the specifi c formation. Only for cases in which core tests or core material are not available should an analog to the formation be used. The core-fl ow database is the fi rst place to look for such an analog.
With all this information collected, a Virtual Lab model can be built for the formation. It includes the effect of radial fl ow from the wellbore. The model can perform sensitivity studies when the well-log data indicate heterogeneity in the formation-mineral compo- sition. Data selected from the reaction and core databases feed into the model.
A stimulation expert selects a few treatment
fl uids based on the information obtained for constructing the model. Each treatment option is simulated. Various injection volumes, rates and shut-in periods can also be evaluated. Uncertainties in the data can be checked by running a sensitivity analysis, which Virtual Lab software can do automatically. With an optimal treatment schedule deter- mined, an operator can now perform the recommended treatment.
If real-time bottomhole pressure data are available during the operation, the treatment design can be adjusted while in progress (below). If operational constraints prevent the treatment from proceeding as planned, the constraints can be put into the model and
Begin treatment
Read real-time data • Bottomhole pressure • Injection rate
• Fluid type and volume injected
the treatment redesigned. Once the design and the operational parameters agree, the real-time data of bottomhole pressure, injec- tion rates and fl uids injected can be compared with model expectations. If there is a signifi cant discrepancy, the model assumptions are reex- amined. For example, the real-time data may provide a new insight into the type, quantity or location of damage, or may suggest that the permeability-porosity relationship in the for- mation differs from that measured in the core. After the model is adjusted, the redesigned treatment can continue. This ability to adjust the model in real time provides a great benefi t in helping operators optimize stimulation jobs. After the treatment, fl owback and produc- tion data can be used to adjust the model one last time. The updated model for that fi eld and reservoir is then available to optimize future treatment jobs.
Do operational
constraints prevent the treatment from being executed as planned?
No Yes Redesign treatment
Adjust model
Do the real-time data match expectations from the model?
Yes
> Real-time feedback loop from process to model. When real-time bottomhole pressure data are available, the model can be adjusted to update the process while it is under way. This feedback loop continues until the job is complete.
No
Check model assumptions
• Formation-damage type, quantity and location
• Permeability-porosity relationship
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Oilfi eld Review
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