Stimulation treatments have been difficult to design and evaluate because of the numerous variables involved. A successful treatment has too long been defined as "one where the treatment was pumped without problems." A successful treatment should be defined as "one that provides the production predicted by the design process." predicted by the design process." The conditions associated with stimulation results and a method to design an optimum treatment with more accurate, predicted results are presented. The method combines both old and new technology presented. The method combines both old and new technology associated with well performance testing (pressure transient testing and production systems analysis), pump-in tests to obtain certain critical variables pump-in tests to obtain certain critical variables in situ, and compilation of the obtained data to design an economically optimum stimulation that will provide predicted results. The method is used to provide predicted results. The method is used to optimize the design prior to spending any money for stimulation treatments. This optimization allows important economic decisions to be made and a treatment design that is based on those economics. The method also allows the evaluation of post-treatment results.


The Industry spends millions of dollars each year for fracture stimulation treatments. The majority of these treatments cannot optimize production because realistic values for the critical production because realistic values for the critical variables used in the treatment design process have not been properly identified. Critical variables are defined as those variables that have the largest impact on the production obtained from any stimulation treatment. Obtaining an accurate value for these variables (as opposed to general estimates) is extremely important for realistic treatment design, execution of the treatment and the final production.

Although the Industry has known the importance of having accurate data for optimization, the tools and technology available to obtain these data have not been utilized. The cost associated with identifying the critical variables may have been a concern in the past; however, current conditions now make it attractive to obtain accurate values for these variables. Other reasons that have contributed to the lack of optimization include the following.

* Methods to determine certain critical variables in situ were not available in the past.

* Treatments providing acceptable results (not necessarily optimum for the reservoir and producing system) become the standard, and the design for those treatments becomes repetitive.

* Change is difficult when we assume the stimulation results in a given area are the best available.

* Evaluation of treatment design and post-treatment results does not command as much time as other production duties.

Optimization is possible with proper identification and utilization of accurate critical variables in the design process.


The critical variables needed to optimize stimulation results can be grouped into three categories. Each category contains variables that are needed to determine specific design criteria.

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